Performance seeking control excitation mode

NASA Technical Reports Server (NTRS)

Schkolnik, Gerard

1995-01-01

Flight testing of the performance seeking control (PSC) excitation mode was successfully completed at NASA Dryden on the F-15 highly integrated digital electronic control (HIDEC) aircraft. Although the excitation mode was not one of the original objectives of the PSC program, it was rapidly prototyped and implemented into the architecture of the PSC algorithm, allowing valuable and timely research data to be gathered. The primary flight test objective was to investigate the feasibility of a future measurement-based performance optimization algorithm. This future algorithm, called AdAPT, which stands for adaptive aircraft performance technology, generates and applies excitation inputs to selected control effectors. Fourier transformations are used to convert measured response and control effector data into frequency domain models which are mapped into state space models using multiterm frequency matching. Formal optimization principles are applied to produce an integrated, performance optimal effector suite. The key technical challenge of the measurement-based approach is the identification of the gradient of the performance index to the selected control effector. This concern was addressed by the excitation mode flight test. The AdAPT feasibility study utilized the PSC excitation mode to apply separate sinusoidal excitation trims to the controls - one aircraft, inlet first ramp (cowl), and one engine, throat area. Aircraft control and response data were recorded using on-board instrumentation and analyzed post-flight. Sensor noise characteristics, axial acceleration performance gradients, and repeatability were determined. Results were compared to pilot comments to assess the ride quality. Flight test results indicate that performance gradients were identified at all flight conditions, sensor noise levels were acceptable at the frequencies of interest, and excitations were generally not sensed by the pilot.

Performance seeking control program overview

NASA Technical Reports Server (NTRS)

Orme, John S.

1995-01-01

The Performance Seeking Control (PSC) program evolved from a series of integrated propulsion-flight control research programs flown at NASA Dryden Flight Research Center (DFRC) on an F-15. The first of these was the Digital Electronic Engine Control (DEEC) program and provided digital engine controls suitable for integration. The DEEC and digital electronic flight control system of the NASA F-15 were ideally suited for integrated controls research. The Advanced Engine Control System (ADECS) program proved that integrated engine and aircraft control could improve overall system performance. The objective of the PSC program was to advance the technology for a fully integrated propulsion flight control system. Whereas ADECS provided single variable control for an average engine, PSC controlled multiple propulsion system variables while adapting to the measured engine performance. PSC was developed as a model-based, adaptive control algorithm and included four optimization modes: minimum fuel flow at constant thrust, minimum turbine temperature at constant thrust, maximum thrust, and minimum thrust. Subsonic and supersonic flight testing were conducted at NASA Dryden covering the four PSC optimization modes and over the full throttle range. Flight testing of the PSC algorithm, conducted in a series of five flight test phases, has been concluded at NASA Dryden covering all four of the PSC optimization modes. Over a three year period and five flight test phases 72 research flights were conducted. The primary objective of flight testing was to exercise each PSC optimization mode and quantify the resulting performance improvements.

Dynamic performance of maximum power point tracking circuits using sinusoidal extremum seeking control for photovoltaic generation

NASA Astrophysics Data System (ADS)

Leyva, R.; Artillan, P.; Cabal, C.; Estibals, B.; Alonso, C.

2011-04-01

The article studies the dynamic performance of a family of maximum power point tracking circuits used for photovoltaic generation. It revisits the sinusoidal extremum seeking control (ESC) technique which can be considered as a particular subgroup of the Perturb and Observe algorithms. The sinusoidal ESC technique consists of adding a small sinusoidal disturbance to the input and processing the perturbed output to drive the operating point at its maximum. The output processing involves a synchronous multiplication and a filtering stage. The filter instance determines the dynamic performance of the MPPT based on sinusoidal ESC principle. The approach uses the well-known root-locus method to give insight about damping degree and settlement time of maximum-seeking waveforms. This article shows the transient waveforms in three different filter instances to illustrate the approach. Finally, an experimental prototype corroborates the dynamic analysis.

A comparison of two adaptive algorithms for the control of active engine mounts

NASA Astrophysics Data System (ADS)

Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.

2005-08-01

This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chertkov, Michael; Turitsyn, Konstantin; Sulc, Petr

The anticipated increase in the number of plug-in electric vehicles (EV) will put additional strain on electrical distribution circuits. Many control schemes have been proposed to control EV charging. Here, we develop control algorithms based on randomized EV charging start times and simple one-way broadcast communication allowing for a time delay between communication events. Using arguments from queuing theory and statistical analysis, we seek to maximize the utilization of excess distribution circuit capacity while keeping the probability of a circuit overload negligible.

A Framework for the Development of Computerized Adaptive Tests

ERIC Educational Resources Information Center

Thompson, Nathan A.; Weiss, David J.

2011-01-01

A substantial amount of research has been conducted over the past 40 years on technical aspects of computerized adaptive testing (CAT), such as item selection algorithms, item exposure controls, and termination criteria. However, there is little literature providing practical guidance on the development of a CAT. This paper seeks to collate some…

Microhole Test Data

DOE Data Explorer

Su, Jiann

2016-05-23

Drilling results from the microhole project at the Sandia High Operating Temperature test facility. The project is seeking to help reduce the cost of exploration and monitoring of geothermal wells and formations by drilling smaller holes. The tests were part of a control algorithm development to optimize the weight-on-bit (WOB) used during drilling with a percussive hammer.

PSC algorithm description

NASA Technical Reports Server (NTRS)

Nobbs, Steven G.

1995-01-01

An overview of the performance seeking control (PSC) algorithm and details of the important components of the algorithm are given. The onboard propulsion system models, the linear programming optimization, and engine control interface are described. The PSC algorithm receives input from various computers on the aircraft including the digital flight computer, digital engine control, and electronic inlet control. The PSC algorithm contains compact models of the propulsion system including the inlet, engine, and nozzle. The models compute propulsion system parameters, such as inlet drag and fan stall margin, which are not directly measurable in flight. The compact models also compute sensitivities of the propulsion system parameters to change in control variables. The engine model consists of a linear steady state variable model (SSVM) and a nonlinear model. The SSVM is updated with efficiency factors calculated in the engine model update logic, or Kalman filter. The efficiency factors are used to adjust the SSVM to match the actual engine. The propulsion system models are mathematically integrated to form an overall propulsion system model. The propulsion system model is then optimized using a linear programming optimization scheme. The goal of the optimization is determined from the selected PSC mode of operation. The resulting trims are used to compute a new operating point about which the optimization process is repeated. This process is continued until an overall (global) optimum is reached before applying the trims to the controllers.

Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells

NASA Astrophysics Data System (ADS)

Spivey, Benjamin James

2011-07-01

Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

PubMed Central

Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A.

2017-01-01

Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm (“profile”) incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes. PMID:28890908

Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting.

PubMed

Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A; Brenton, Ashley

2017-01-01

Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.

Controlling the Internal Heat Transfer Coefficient by the Characteristics of External Flows

NASA Astrophysics Data System (ADS)

Zhuromskii, V. M.

2018-01-01

The engineering-physical fundamentals of substance synthesis in a boiling apparatus are presented. We have modeled a system of automatic stabilization of the maximum internal heat transfer coefficient in such an apparatus by the characteristics of external flows on the basis of adaptive seeking algorithms. The results of operation of the system in the shop are presented.

Cooperating or fighting with control noise in the optimal manipulation of quantum dynamics

NASA Astrophysics Data System (ADS)

Shuang, Feng; Rabitz, Herschel

2004-11-01

This paper investigates the impact of control field noise on the optimal manipulation of quantum dynamics. Simulations are performed on several multilevel quantum systems with the goal of population transfer in the presence of significant control noise. The noise enters as run-to-run variations in the control amplitude and phase with the observation being an ensemble average over many runs as is commonly done in the laboratory. A genetic algorithm with an improved elitism operator is used to find the optimal field that either fights against or cooperates with control field noise. When seeking a high control yield it is possible to find fields that successfully fight with the noise while attaining good quality stable results. When seeking modest control yields, fields can be found which are optimally shaped to cooperate with the noise and thereby drive the dynamics more efficiently. In general, noise reduces the coherence of the dynamics, but the results indicate that population transfer objectives can be met by appropriately either fighting or cooperating with noise, even when it is intense.

Cooperating or fighting with control noise in the optimal manipulation of quantum dynamics.

PubMed

Shuang, Feng; Rabitz, Herschel

2004-11-15

This paper investigates the impact of control field noise on the optimal manipulation of quantum dynamics. Simulations are performed on several multilevel quantum systems with the goal of population transfer in the presence of significant control noise. The noise enters as run-to-run variations in the control amplitude and phase with the observation being an ensemble average over many runs as is commonly done in the laboratory. A genetic algorithm with an improved elitism operator is used to find the optimal field that either fights against or cooperates with control field noise. When seeking a high control yield it is possible to find fields that successfully fight with the noise while attaining good quality stable results. When seeking modest control yields, fields can be found which are optimally shaped to cooperate with the noise and thereby drive the dynamics more efficiently. In general, noise reduces the coherence of the dynamics, but the results indicate that population transfer objectives can be met by appropriately either fighting or cooperating with noise, even when it is intense.

e-DMDAV: A new privacy preserving algorithm for wearable enterprise information systems

NASA Astrophysics Data System (ADS)

Zhang, Zhenjiang; Wang, Xiaoni; Uden, Lorna; Zhang, Peng; Zhao, Yingsi

2018-04-01

Wearable devices have been widely used in many fields to improve the quality of people's lives. More and more data on individuals and businesses are collected by statistical organizations though those devices. Almost all of this data holds confidential information. Statistical Disclosure Control (SDC) seeks to protect statistical data in such a way that it can be released without giving away confidential information that can be linked to specific individuals or entities. The MDAV (Maximum Distance to Average Vector) algorithm is an efficient micro-aggregation algorithm belonging to SDC. However, the MDAV algorithm cannot survive homogeneity and background knowledge attacks because it was designed for static numerical data. This paper proposes a systematic dynamic-updating anonymity algorithm based on MDAV called the e-DMDAV algorithm. This algorithm introduces a new parameter and a table to ensure that the k records in one cluster with the range of the distinct values in each cluster is no less than e for numerical and non-numerical datasets. This new algorithm has been evaluated and compared with the MDAV algorithm. The simulation results show that the new algorithm outperforms MDAV in terms of minimizing distortion and disclosure risk with a similar computational cost.

Can genetic algorithms help virus writers reshape their creations and avoid detection?

NASA Astrophysics Data System (ADS)

Abu Doush, Iyad; Al-Saleh, Mohammed I.

2017-11-01

Different attack and defence techniques have been evolved over time as actions and reactions between black-hat and white-hat communities. Encryption, polymorphism, metamorphism and obfuscation are among the techniques used by the attackers to bypass security controls. On the other hand, pattern matching, algorithmic scanning, emulation and heuristic are used by the defence team. The Antivirus (AV) is a vital security control that is used against a variety of threats. The AV mainly scans data against its database of virus signatures. Basically, it claims a virus if a match is found. This paper seeks to find the minimal possible changes that can be made on the virus so that it will appear normal when scanned by the AV. Brute-force search through all possible changes can be a computationally expensive task. Alternatively, this paper tries to apply a Genetic Algorithm in solving such a problem. Our proposed algorithm is tested on seven different malware instances. The results show that in all the tested malware instances only a small change in each instance was good enough to bypass the AV.

Thrust stand evaluation of engine performance improvement algorithms in an F-15 airplane

NASA Technical Reports Server (NTRS)

Conners, Timothy R.

1992-01-01

An investigation is underway to determine the benefits of a new propulsion system optimization algorithm in an F-15 airplane. The performance seeking control (PSC) algorithm optimizes the quasi-steady-state performance of an F100 derivative turbofan engine for several modes of operation. The PSC algorithm uses an onboard software engine model that calculates thrust, stall margin, and other unmeasured variables for use in the optimization. As part of the PSC test program, the F-15 aircraft was operated on a horizontal thrust stand. Thrust was measured with highly accurate load cells. The measured thrust was compared to onboard model estimates and to results from posttest performance programs. Thrust changes using the various PSC modes were recorded. Those results were compared to benefits using the less complex highly integrated digital electronic control (HIDEC) algorithm. The PSC maximum thrust mode increased intermediate power thrust by 10 percent. The PSC engine model did very well at estimating measured thrust and closely followed the transients during optimization. Quantitative results from the evaluation of the algorithms and performance calculation models are included with emphasis on measured thrust results. The report presents a description of the PSC system and a discussion of factors affecting the accuracy of the thrust stand load measurements.

Identification of observer/Kalman filter Markov parameters: Theory and experiments

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh; Horta, Lucas G.; Longman, Richard W.

1991-01-01

An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on an ten-bay truss structure.

Optimal path planning for a mobile robot using cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Mohanty, Prases K.; Parhi, Dayal R.

2016-03-01

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.

Error field optimization in DIII-D using extremum seeking control

NASA Astrophysics Data System (ADS)

Lanctot, M. J.; Olofsson, K. E. J.; Capella, M.; Humphreys, D. A.; Eidietis, N.; Hanson, J. M.; Paz-Soldan, C.; Strait, E. J.; Walker, M. L.

2016-07-01

DIII-D experiments have demonstrated a new real-time approach to tokamak error field control based on maximizing the toroidal angular momentum. This approach uses extremum seeking control theory to optimize the error field in real time without inducing instabilities. Slowly-rotating n  =  1 fields (the dither), generated by external coils, are used to perturb the angular momentum, monitored in real-time using a charge-exchange spectroscopy diagnostic. Simple signal processing of the rotation measurements extracts information about the rotation gradient with respect to the control coil currents. This information is used to converge the control coil currents to a point that maximizes the toroidal angular momentum. The technique is well-suited for multi-coil, multi-harmonic error field optimizations in disruption sensitive devices as it does not require triggering locked tearing modes or plasma current disruptions. Control simulations highlight the importance of the initial search direction on the rate of the convergence, and identify future algorithm upgrades that may allow more rapid convergence that projects to convergence times in ITER on the order of tens of seconds.

Closed Loop System Identification with Genetic Algorithms

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

Different types of maximum power point tracking techniques for renewable energy systems: A survey

NASA Astrophysics Data System (ADS)

Khan, Mohammad Junaid; Shukla, Praveen; Mustafa, Rashid; Chatterji, S.; Mathew, Lini

2016-03-01

Global demand for electricity is increasing while production of energy from fossil fuels is declining and therefore the obvious choice of the clean energy source that is abundant and could provide security for development future is energy from the sun. In this paper, the characteristic of the supply voltage of the photovoltaic generator is nonlinear and exhibits multiple peaks, including many local peaks and a global peak in non-uniform irradiance. To keep global peak, MPPT is the important component of photovoltaic systems. Although many review articles discussed conventional techniques such as P & O, incremental conductance, the correlation ripple control and very few attempts have been made with intelligent MPPT techniques. This document also discusses different algorithms based on fuzzy logic, Ant Colony Optimization, Genetic Algorithm, artificial neural networks, Particle Swarm Optimization Algorithm Firefly, Extremum seeking control method and hybrid methods applied to the monitoring of maximum value of power at point in systems of photovoltaic under changing conditions of irradiance.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Flight assessment of the onboard propulsion system model for the Performance Seeking Control algorithm on an F-15 aircraft

NASA Technical Reports Server (NTRS)

Orme, John S.; Schkolnik, Gerard S.

1995-01-01

Performance Seeking Control (PSC), an onboard, adaptive, real-time optimization algorithm, relies upon an onboard propulsion system model. Flight results illustrated propulsion system performance improvements as calculated by the model. These improvements were subject to uncertainty arising from modeling error. Thus to quantify uncertainty in the PSC performance improvements, modeling accuracy must be assessed. A flight test approach to verify PSC-predicted increases in thrust (FNP) and absolute levels of fan stall margin is developed and applied to flight test data. Application of the excess thrust technique shows that increases of FNP agree to within 3 percent of full-scale measurements for most conditions. Accuracy to these levels is significant because uncertainty bands may now be applied to the performance improvements provided by PSC. Assessment of PSC fan stall margin modeling accuracy was completed with analysis of in-flight stall tests. Results indicate that the model overestimates the stall margin by between 5 to 10 percent. Because PSC achieves performance gains by using available stall margin, this overestimation may represent performance improvements to be recovered with increased modeling accuracy. Assessment of thrust and stall margin modeling accuracy provides a critical piece for a comprehensive understanding of PSC's capabilities and limitations.

C-reactive protein testing does not decrease antibiotic use for acute cough illness when compared to a clinical algorithm.

PubMed

Gonzales, Ralph; Aagaard, Eva M; Camargo, Carlos A; Ma, O John; Plautz, Mark; Maselli, Judith H; McCulloch, Charles E; Levin, Sara K; Metlay, Joshua P

2011-07-01

Antibiotics are commonly overused in adults seeking emergency department (ED) care for acute cough illness. To evaluate the effect of a point-of-care C-reactive protein (CRP) blood test on antibiotic treatment of acute cough illness in adults. A randomized controlled trial was conducted in a single urban ED in the United States. The participants were adults (age ≥ 18 years) seeking care for acute cough illness (≤ 21 days duration); 139 participants were enrolled, and 131 completed the ED visit. Between November 2005 and March 2006, study participants had attached to their medical charts a clinical algorithm with recommendations for chest X-ray study or antibiotic treatment. For CRP-tested patients, recommendations were based on the same algorithm plus the CRP level. There was no difference in antibiotic use between CRP-tested and control participants (37% [95% confidence interval (CI) 29-45%] vs. 31% [95% CI 23-39%], respectively; p = 0.46) or chest X-ray use (52% [95% CI 43-61%] vs. 48% [95% CI 39-57%], respectively; p = 0.67). Among CRP-tested participants, those with normal CRP levels received antibiotics much less frequently than those with indeterminate CRP levels (20% [95% CI 7-33%] vs. 50% [95% CI 32-68%], respectively; p = 0.01). Point-of-care CRP testing does not seem to provide any additional value beyond a point-of-care clinical decision support for reducing antibiotic use in adults with acute cough illness. Copyright © 2011 Elsevier Inc. All rights reserved.

Learning in engineered multi-agent systems

NASA Astrophysics Data System (ADS)

Menon, Anup

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power---as is the case in present-day wind farms---does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed graph (communication graph) and, under certain conditions, prove convergence of agent joint action (under eITEL) to the welfare optimizing set. The main condition requires that the union of interaction and communication graphs be strongly connected; thus the algorithm combines an implicit form of communication (via interactions through utility functions) with explicit inter-agent communications to achieve the given collaborative goal. This work has kinship with certain evolutionary computation techniques such as Simulated Annealing; the algorithm steps are carefully designed such that it describes an ergodic Markov chain with a stationary distribution that has support over states where agent joint actions optimize the welfare function. The main analysis tool is perturbed Markov chains and results of broader interest regarding these are derived as well. The other algorithm, Collaborative Extremum Seeking (CES), uses techniques from extremum seeking control to solve the problem when agent actions are drawn from the set of real numbers. In this case, under the assumption of existence of a local minimizer for the welfare function and a connected undirected communication graph between agents, a result regarding convergence of joint action to a small neighborhood of a local optimizer of the welfare function is proved. Since extremum seeking control uses a simultaneous gradient estimation-descent scheme, gradient information available in the continuous action space formulation is exploited by the CES algorithm to yield improved convergence speeds. The effectiveness of this algorithm for the wind farm power maximization problem is evaluated via simulations. Lastly, we turn to a different question regarding role of the information exchange pattern on performance of distributed control systems by means of a case study for the vehicle platooning problem. In the vehicle platoon control problem, the objective is to design distributed control laws for individual vehicles in a platoon (or a road-train) that regulate inter-vehicle distances at a specified safe value while the entire platoon follows a leader-vehicle. While most of the literature on the problem deals with some inadequacy in control performance when the information exchange is of the nearest neighbor-type, we consider an arbitrary graph serving as information exchange pattern and derive a relationship between how a certain indicator of control performance is related to the information pattern. Such analysis helps in understanding qualitative features of the `right' information pattern for this problem.

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.

PubMed

Brenton, Ashley; Richeimer, Steven; Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Blanchard, John; Meshkin, Brian

2017-01-01

Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes.

SU-E-T-33: A Feasibility-Seeking Algorithm Applied to Planning of Intensity Modulated Proton Therapy: A Proof of Principle Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Penfold, S; Casiraghi, M; Dou, T

2015-06-15

Purpose: To investigate the applicability of feasibility-seeking cyclic orthogonal projections to the field of intensity modulated proton therapy (IMPT) inverse planning. Feasibility of constraints only, as opposed to optimization of a merit function, is less demanding algorithmically and holds a promise of parallel computations capability with non-cyclic orthogonal projections algorithms such as string-averaging or block-iterative strategies. Methods: A virtual 2D geometry was designed containing a C-shaped planning target volume (PTV) surrounding an organ at risk (OAR). The geometry was pixelized into 1 mm pixels. Four beams containing a subset of proton pencil beams were simulated in Geant4 to provide themore » system matrix A whose elements a-ij correspond to the dose delivered to pixel i by a unit intensity pencil beam j. A cyclic orthogonal projections algorithm was applied with the goal of finding a pencil beam intensity distribution that would meet the following dose requirements: D-OAR < 54 Gy and 57 Gy < D-PTV < 64.2 Gy. The cyclic algorithm was based on the concept of orthogonal projections onto half-spaces according to the Agmon-Motzkin-Schoenberg algorithm, also known as ‘ART for inequalities’. Results: The cyclic orthogonal projections algorithm resulted in less than 5% of the PTV pixels and less than 1% of OAR pixels violating their dose constraints, respectively. Because of the abutting OAR-PTV geometry and the realistic modelling of the pencil beam penumbra, complete satisfaction of the dose objectives was not achieved, although this would be a clinically acceptable plan for a meningioma abutting the brainstem, for example. Conclusion: The cyclic orthogonal projections algorithm was demonstrated to be an effective tool for inverse IMPT planning in the 2D test geometry described. We plan to further develop this linear algorithm to be capable of incorporating dose-volume constraints into the feasibility-seeking algorithm.« less

Optimal wavefront estimation of incoherent sources

NASA Astrophysics Data System (ADS)

Riggs, A. J. Eldorado; Kasdin, N. Jeremy; Groff, Tyler

2014-08-01

Direct imaging is in general necessary to characterize exoplanets and disks. A coronagraph is an instrument used to create a dim (high-contrast) region in a star's PSF where faint companions can be detected. All coronagraphic high-contrast imaging systems use one or more deformable mirrors (DMs) to correct quasi-static aberrations and recover contrast in the focal plane. Simulations show that existing wavefront control algorithms can correct for diffracted starlight in just a few iterations, but in practice tens or hundreds of control iterations are needed to achieve high contrast. The discrepancy largely arises from the fact that simulations have perfect knowledge of the wavefront and DM actuation. Thus, wavefront correction algorithms are currently limited by the quality and speed of wavefront estimates. Exposures in space will take orders of magnitude more time than any calculations, so a nonlinear estimation method that needs fewer images but more computational time would be advantageous. In addition, current wavefront correction routines seek only to reduce diffracted starlight. Here we present nonlinear estimation algorithms that include optimal estimation of sources incoherent with a star such as exoplanets and debris disks.

Servo Platform Circuit Design of Pendulous Gyroscope Based on DSP

NASA Astrophysics Data System (ADS)

Tan, Lilong; Wang, Pengcheng; Zhong, Qiyuan; Zhang, Cui; Liu, Yunfei

2018-03-01

In order to solve the problem when a certain type of pendulous gyroscope in the initial installation deviation more than 40 degrees, that the servo platform can not be up to the speed of the gyroscope in the rough north seeking phase. This paper takes the digital signal processor TMS320F28027 as the core, uses incremental digital PID algorithm, carries out the circuit design of the servo platform. Firstly, the hardware circuit is divided into three parts: DSP minimum system, motor driving circuit and signal processing circuit, then the mathematical model of incremental digital PID algorithm is established, based on the model, writes the PID control program in CCS3.3, finally, the servo motor tracking control experiment is carried out, it shows that the design can significantly improve the tracking ability of the servo platform, and the design has good engineering practice.

Optimal Control of Electrodynamic Tethers

DTIC Science & Technology

2008-06-01

the out-of- plane component of the magnetic field which produces the required in- track thrust is reduced (see Eq. as th sp lo e a (4... the same orbital plane using an EDT. For the sake of testing the algorithm against a known solution we seek the maximum altitude an EDT can reach...be incl planes co gnetic equatorial plane ent of latit and m mi ains constant as the EDT ity the Eart e tilted with respect to the

Symbolic LTL Compilation for Model Checking: Extended Abstract

NASA Technical Reports Server (NTRS)

Rozier, Kristin Y.; Vardi, Moshe Y.

2007-01-01

In Linear Temporal Logic (LTL) model checking, we check LTL formulas representing desired behaviors against a formal model of the system designed to exhibit these behaviors. To accomplish this task, the LTL formulas must be translated into automata [21]. We focus on LTL compilation by investigating LTL satisfiability checking via a reduction to model checking. Having shown that symbolic LTL compilation algorithms are superior to explicit automata construction algorithms for this task [16], we concentrate here on seeking a better symbolic algorithm.We present experimental data comparing algorithmic variations such as normal forms, encoding methods, and variable ordering and examine their effects on performance metrics including processing time and scalability. Safety critical systems, such as air traffic control, life support systems, hazardous environment controls, and automotive control systems, pervade our daily lives, yet testing and simulation alone cannot adequately verify their reliability [3]. Model checking is a promising approach to formal verification for safety critical systems which involves creating a formal mathematical model of the system and translating desired safety properties into a formal specification for this model. The complement of the specification is then checked against the system model. When the model does not satisfy the specification, model-checking tools accompany this negative answer with a counterexample, which points to an inconsistency between the system and the desired behaviors and aids debugging efforts.

Restructured Freedom configuration characteristics

NASA Technical Reports Server (NTRS)

Troutman, Patrick A.; Heck, Michael L.; Kumar, Renjith R.; Mazanek, Daniel D.

1991-01-01

In Jan. 1991, the LaRc SSFO performed an assessment of the configuration characteristics of the proposed pre-integrated Space Station Freedom (SSF) concept. Of particular concern was the relationship of solar array operation and orientation with respect to spacecraft controllability. For the man-tended configuration (MTC), it was determined that torque equilibrium attitude (TEA) seeking Control Moment Gyroscope (CMG) control laws could not always maintain attitude. The control problems occurred when the solar arrays were tracking the sun to produce full power while flying in an arrow or gravity gradient flight mode. The large solar array articulations that sometimes result from having the functions of the alpha and beta joints reversed on MTC induced large product of inertia changes that can invalidate the control system gains during an orbit. Several modified sun tracking techniques were evaluated with respect to producing a controllable configuration requiring no modifications to the CMG control algorithms. Another assessment involved the permanently manned configuration (PMC) which has a third asymmetric PV unit on one side of the transverse boom. Recommendations include constraining alpha rotations for MTC in the arrow and gravity gradient flight modes and perhaps developing new non-TEA seeking control laws. Recommendations for PMC include raising the operational altitude and moving to a symmetric configuration as soon as possible.

Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash.

PubMed

Pelletier, Mathew G

2008-02-08

One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU) as an alternative to thePC's traditional use of the central processing unit (CPU). The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit "GPU", for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC's central processing unit "CPU", wasgained. The new parallel algorithm operating on the GPU was able to process a 1024x1024image in less than 17ms. At this improved speed, the image processing system's performance should now be sufficient to provide a system that would be capable of realtimefeed-back control that is in tight cooperation with the cleaning equipment.

Development of a Control Optimization System for Real Time Monitoring of Managed Aquifer Recharge and Recovery Systems Using Intelligent Sensors

NASA Astrophysics Data System (ADS)

Smits, K. M.; Drumheller, Z. W.; Lee, J. H.; Illangasekare, T. H.; Regnery, J.; Kitanidis, P. K.

2015-12-01

Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization lead to reduced natural recharge rates and overuse. Scientists and engineers have begun to revisit the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. This research seeks to develop and validate a general simulation-based control optimization algorithm that relies on real-time data collected though embedded sensors that can be used to ease the operational challenges of MAR facilities. Experiments to validate the control algorithm were conducted at the laboratory scale in a two-dimensional synthetic aquifer under both homogeneous and heterogeneous packing configurations. The synthetic aquifer used well characterized technical sands and the electrical conductivity signal of an inorganic conservative tracer as a surrogate measure for water quality. The synthetic aquifer was outfitted with an array of sensors and an autonomous pumping system. Experimental results verified the feasibility of the approach and suggested that the system can improve the operation of MAR facilities. The dynamic parameter inversion reduced the average error between the simulated and observed pressures between 12.5 and 71.4%. The control optimization algorithm ran smoothly and generated optimal control decisions. Overall, results suggest that with some improvements to the inversion and interpolation algorithms, which can be further advanced through testing with laboratory experiments using sensors, the concept can successfully improve the operation of MAR facilities.

Towards Internet QoS provisioning based on generic distributed QoS adaptive routing engine.

PubMed

Haikal, Amira Y; Badawy, M; Ali, Hesham A

2014-01-01

Increasing efficiency and quality demands of modern Internet technologies drive today's network engineers to seek to provide quality of service (QoS). Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE) architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i) provide a general configuration guideline for service differentiation, (ii) formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA) based on dynamic programming technique, and (iii) propose QoS multipath forwarding (QMPF) model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature.

Towards Internet QoS Provisioning Based on Generic Distributed QoS Adaptive Routing Engine

PubMed Central

Haikal, Amira Y.; Badawy, M.; Ali, Hesham A.

2014-01-01

Increasing efficiency and quality demands of modern Internet technologies drive today's network engineers to seek to provide quality of service (QoS). Internet QoS provisioning gives rise to several challenging issues. This paper introduces a generic distributed QoS adaptive routing engine (DQARE) architecture based on OSPFxQoS. The innovation of the proposed work in this paper is its undependability on the used QoS architectures and, moreover, splitting of the control strategy from data forwarding mechanisms, so we guarantee a set of absolute stable mechanisms on top of which Internet QoS can be built. DQARE architecture is furnished with three relevant traffic control schemes, namely, service differentiation, QoS routing, and traffic engineering. The main objective of this paper is to (i) provide a general configuration guideline for service differentiation, (ii) formalize the theoretical properties of different QoS routing algorithms and then introduce a QoS routing algorithm (QOPRA) based on dynamic programming technique, and (iii) propose QoS multipath forwarding (QMPF) model for paths diversity exploitation. NS2-based simulations proved the DQARE superiority in terms of delay, packet delivery ratio, throughput, and control overhead. Moreover, extensive simulations are used to compare the proposed QOPRA algorithm and QMPF model with their counterparts in the literature. PMID:25309955

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

PubMed Central

Zhu, Qian; Wong, Aaron K; Krishnan, Arjun; Aure, Miriam R; Tadych, Alicja; Zhang, Ran; Corney, David C; Greene, Casey S; Bongo, Lars A; Kristensen, Vessela N; Charikar, Moses; Li, Kai; Troyanskaya, Olga G.

2016-01-01

We present SEEK (http://seek.princeton.edu), a query-based search engine across very large transcriptomic data collections, including thousands of human data sets from almost 50 microarray and next-generation sequencing platforms. SEEK uses a novel query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify query-coregulated genes, pathways, and processes. SEEK provides cross-platform handling, multi-gene query search, iterative metadata-based search refinement, and extensive visualization-based analysis options. PMID:25581801

An Optimization System with Parallel Processing for Reducing Common-Mode Current on Electronic Control Unit

NASA Astrophysics Data System (ADS)

Okazaki, Yuji; Uno, Takanori; Asai, Hideki

In this paper, we propose an optimization system with parallel processing for reducing electromagnetic interference (EMI) on electronic control unit (ECU). We adopt simulated annealing (SA), genetic algorithm (GA) and taboo search (TS) to seek optimal solutions, and a Spice-like circuit simulator to analyze common-mode current. Therefore, the proposed system can determine the adequate combinations of the parasitic inductance and capacitance values on printed circuit board (PCB) efficiently and practically, to reduce EMI caused by the common-mode current. Finally, we apply the proposed system to an example circuit to verify the validity and efficiency of the system.

Hybrid Stochastic Search Technique based Suboptimal AGC Regulator Design for Power System using Constrained Feedback Control Strategy

NASA Astrophysics Data System (ADS)

Ibraheem, Omveer, Hasan, N.

2010-10-01

A new hybrid stochastic search technique is proposed to design of suboptimal AGC regulator for a two area interconnected non reheat thermal power system incorporating DC link in parallel with AC tie-line. In this technique, we are proposing the hybrid form of Genetic Algorithm (GA) and simulated annealing (SA) based regulator. GASA has been successfully applied to constrained feedback control problems where other PI based techniques have often failed. The main idea in this scheme is to seek a feasible PI based suboptimal solution at each sampling time. The feasible solution decreases the cost function rather than minimizing the cost function.

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

PubMed Central

Brenton, Ashley; Richeimer, Steven; Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Blanchard, John; Meshkin, Brian

2017-01-01

Background Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs). Patients and methods The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes. PMID:28572737

Monte Carlo Solution to Find Input Parameters in Systems Design Problems

NASA Astrophysics Data System (ADS)

Arsham, Hossein

2013-06-01

Most engineering system designs, such as product, process, and service design, involve a framework for arriving at a target value for a set of experiments. This paper considers a stochastic approximation algorithm for estimating the controllable input parameter within a desired accuracy, given a target value for the performance function. Two different problems, what-if and goal-seeking problems, are explained and defined in an auxiliary simulation model, which represents a local response surface model in terms of a polynomial. A method of constructing this polynomial by a single run simulation is explained. An algorithm is given to select the design parameter for the local response surface model. Finally, the mean time to failure (MTTF) of a reliability subsystem is computed and compared with its known analytical MTTF value for validation purposes.

Determination of feature generation methods for PTZ camera object tracking

NASA Astrophysics Data System (ADS)

Doyle, Daniel D.; Black, Jonathan T.

2012-06-01

Object detection and tracking using computer vision (CV) techniques have been widely applied to sensor fusion applications. Many papers continue to be written that speed up performance and increase learning of artificially intelligent systems through improved algorithms, workload distribution, and information fusion. Military application of real-time tracking systems is becoming more and more complex with an ever increasing need of fusion and CV techniques to actively track and control dynamic systems. Examples include the use of metrology systems for tracking and measuring micro air vehicles (MAVs) and autonomous navigation systems for controlling MAVs. This paper seeks to contribute to the determination of select tracking algorithms that best track a moving object using a pan/tilt/zoom (PTZ) camera applicable to both of the examples presented. The select feature generation algorithms compared in this paper are the trained Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the Mixture of Gaussians (MoG) background subtraction method, the Lucas- Kanade optical flow method (2000) and the Farneback optical flow method (2003). The matching algorithm used in this paper for the trained feature generation algorithms is the Fast Library for Approximate Nearest Neighbors (FLANN). The BSD licensed OpenCV library is used extensively to demonstrate the viability of each algorithm and its performance. Initial testing is performed on a sequence of images using a stationary camera. Further testing is performed on a sequence of images such that the PTZ camera is moving in order to capture the moving object. Comparisons are made based upon accuracy, speed and memory.

Simulation evaluation of a low-altitude helicopter flight guidance system adapted for a helmet-mounted display

NASA Technical Reports Server (NTRS)

Swenson, Harry N.; Zelenka, Richard E.; Hardy, Gordon H.; Dearing, Munro G.

1992-01-01

A computer aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generation algorithm based upon dynamic programming and a helmet-mounted display (HMD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor guidance symbology. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission way points that seeks valleys to minimize threat exposure. The pilot evaluation was conducted at NASA ARC moving base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, the Air Force, and the helicopter industry. The pilots manually tracked the trajectory generated by the algorithm utilizing the HMD symbology. The pilots were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

Computer aiding for low-altitude helicopter flight

NASA Technical Reports Server (NTRS)

Swenson, Harry N.

1991-01-01

A computer-aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generated algorithm based on dynamic programming, and a head-up display (HUD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor symbol. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission waypoints that minimizes threat exposure by seeking valleys. The pilot evaluation was conducted at NASA Ames Research Center's Sim Lab facility in both the fixed-base Interchangeable Cab (ICAB) simulator and the moving-base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, and the U.S. Air Force. The pilots manually tracked the trajectory generated by the algorithm utilizing the HUD symbology. They were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

Intelligent Control for Drag Reduction on the X-48B Vehicle

NASA Technical Reports Server (NTRS)

Griffin, Brian Joseph; Brown, Nelson Andrew; Yoo, Seung Yeun

2011-01-01

This paper focuses on the development of an intelligent control technology for in-flight drag reduction. The system is integrated with and demonstrated on the full X-48B nonlinear simulation. The intelligent control system utilizes a peak-seeking control method implemented with a time-varying Kalman filter. Performance functional coordinate and magnitude measurements, or independent and dependent parameters respectively, are used by the Kalman filter to provide the system with gradient estimates of the designed performance function which is used to drive the system toward a local minimum in a steepestdescent approach. To ensure ease of integration and algorithm performance, a single-input single-output approach was chosen. The framework, specific implementation considerations, simulation results, and flight feasibility issues related to this platform are discussed.

Stochastic control approaches for sensor management in search and exploitation

NASA Astrophysics Data System (ADS)

Hitchings, Darin Chester

Recent improvements in the capabilities of autonomous vehicles have motivated their increased use in such applications as defense, homeland security, environmental monitoring, and surveillance. To enhance performance in these applications, new algorithms are required to control teams of robots autonomously and through limited interactions with human operators. In this dissertation we develop new algorithms for control of robots performing information-seeking missions in unknown environments. These missions require robots to control their sensors in order to discover the presence of objects, keep track of the objects, and learn what these objects are, given a fixed sensing budget. Initially, we investigate control of multiple sensors, with a finite set of sensing options and finite-valued measurements, to locate and classify objects given a limited resource budget. The control problem is formulated as a Partially Observed Markov Decision Problem (POMDP), but its exact solution requires excessive computation. Under the assumption that sensor error statistics are independent and time-invariant, we develop a class of algorithms using Lagrangian Relaxation techniques to obtain optimal mixed strategies using performance bounds developed in previous research. We investigate alternative Receding Horizon (RH) controllers to convert the mixed strategies to feasible adaptive-sensing strategies and evaluate the relative performance of these controllers in simulation. The resulting controllers provide superior performance to alternative algorithms proposed in the literature and obtain solutions to large-scale POMDP problems several orders of magnitude faster than optimal Dynamic Programming (DP) approaches with comparable performance quality. We extend our results for finite action, finite measurement sensor control to scenarios with moving objects. We use Hidden Markov Models (HMMs) for the evolution of objects, according to the dynamics of a birth-death process. We develop a new lower bound on the performance of adaptive controllers in these scenarios, develop algorithms for computing solutions to this lower bound, and use these algorithms as part of a RH controller for sensor allocation in the presence of moving objects We also consider an adaptive Search problem where sensing actions are continuous and the underlying measurement space is also continuous. We extend our previous hierarchical decomposition approach based on performance bounds to this problem and develop novel implementations of Stochastic Dynamic Programming (SDP) techniques to solve this problem. Our algorithms are nearly two orders of magnitude faster than previously proposed approaches and yield solutions of comparable quality. For supervisory control, we discuss how human operators can work with and augment robotic teams performing these tasks. Our focus is on how tasks are partitioned among teams of robots and how a human operator can make intelligent decisions for task partitioning. We explore these questions through the design of a game that involves robot automata controlled by our algorithms and a human supervisor that partitions tasks based on different levels of support information. This game can be used with human subject experiments to explore the effect of information on quality of supervisory control.

Adaptive and Resilient Soft Tensegrity Robots.

PubMed

Rieffel, John; Mouret, Jean-Baptiste

2018-04-17

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control-and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This article describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics to generate a wealth of dynamical behaviors.

Variable-Metric Algorithm For Constrained Optimization

NASA Technical Reports Server (NTRS)

Frick, James D.

1989-01-01

Variable Metric Algorithm for Constrained Optimization (VMACO) is nonlinear computer program developed to calculate least value of function of n variables subject to general constraints, both equality and inequality. First set of constraints equality and remaining constraints inequalities. Program utilizes iterative method in seeking optimal solution. Written in ANSI Standard FORTRAN 77.

Human-like machines: Transparency and comprehensibility.

PubMed

Patrzyk, Piotr M; Link, Daniela; Marewski, Julian N

2017-01-01

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

A Maximum Power Point Tracking Control Method of a Photovoltaic Power Generator with Consideration of Dynamic Characteristics of Solar Cells

NASA Astrophysics Data System (ADS)

Watanabe, Takashi; Yoshida, Toshiya; Ohniwa, Katsumi

This paper discusses a new control strategy for photovoltaic power generation systems with consideration of dynamic characteristics of the photovoltaic cells. The controller estimates internal currents of an equivalent circuit for the cells. This estimated, or the virtual current and the actual voltage of the cells are fed to a conventional Maximum-Power-Point-Tracking (MPPT) controller. Consequently, this MPPT controller still tracks the optimum point even though it is so designed that the seeking speed of the operating point is extremely high. This system may suit for applications, which are installed in rapidly changeable insolation and temperature-conditions e.g. automobiles, trains, and airplanes. The proposed method is verified by experiment with a combination of this estimating function and the modified Boehringer's MPPT algorithm.

Cognitive programs: software for attention's executive

PubMed Central

Tsotsos, John K.; Kruijne, Wouter

2014-01-01

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention. PMID:25505430

Consensus seeking, formation keeping, and trajectory tracking in multiple vehicle cooperative control

NASA Astrophysics Data System (ADS)

Ren, Wei

Cooperative control problems for multiple vehicle systems can be categorized as either formation control problems with applications to mobile robots, unmanned air vehicles, autonomous underwater vehicles, satellites, aircraft, spacecraft, and automated highway systems, or non-formation control problems such as task assignment, cooperative transport, cooperative role assignment, air traffic control, cooperative timing, and cooperative search. The cooperative control of multiple vehicle systems poses significant theoretical and practical challenges. For cooperative control strategies to be successful, numerous issues must be addressed. We consider three important and correlated issues: consensus seeking, formation keeping, and trajectory tracking. For consensus seeking, we investigate algorithms and protocols so that a team of vehicles can reach consensus on the values of the coordination data in the presence of imperfect sensors, communication dropout, sparse communication topologies, and noisy and unreliable communication links. The main contribution of this dissertation in this area is that we show necessary and/or sufficient conditions for consensus seeking with limited, unidirectional, and unreliable information exchange under fixed and switching interaction topologies (through either communication or sensing). For formation keeping, we apply a so-called "virtual structure" approach to spacecraft formation flying and multi-vehicle formation maneuvers. As a result, single vehicle path planning and trajectory generation techniques can be employed for the virtual structure while trajectory tracking strategies can be employed for each vehicle. The main contribution of this dissertation in this area is that we propose a decentralized architecture for multiple spacecraft formation flying in deep space with formation feedback introduced. This architecture ensures the necessary precision in the presence of actuator saturation, internal and external disturbances, and stringent inter-vehicle communication limitations. A constructive approach based on the satisficing control paradigm is also applied to multi-robot coordination in hardware. For trajectory tracking, we investigate nonlinear tracking controllers for fixed wing unmanned air vehicles and nonholonomic mobile robots with velocity and heading rate constraints. The main contribution of this dissertation in this area is that our proposed tracking controllers are shown to be robust to input uncertainties and measurement noise, and are computationally simple and can be implemented with low-cost, low-power microcontrollers. In addition, our approach allows piecewise continuous reference velocity and heading rate and can be extended to derive a variety of other trajectory tracking strategies.

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

Can Linear Superiorization Be Useful for Linear Optimization Problems?

PubMed Central

Censor, Yair

2017-01-01

Linear superiorization considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not necessarily minimal) target function values. The two questions that we set out to explore experimentally are (i) Does linear superiorization provide a feasible point whose linear target function value is lower than that obtained by running the same feasibility-seeking algorithm without superiorization under identical conditions? and (ii) How does linear superiorization fare in comparison with the Simplex method for solving linear programming problems? Based on our computational experiments presented here, the answers to these two questions are: “yes” and “very well”, respectively. PMID:29335660

Can linear superiorization be useful for linear optimization problems?

NASA Astrophysics Data System (ADS)

Censor, Yair

2017-04-01

Linear superiorization (LinSup) considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not necessarily minimal) target function values. The two questions that we set out to explore experimentally are: (i) does LinSup provide a feasible point whose linear target function value is lower than that obtained by running the same feasibility-seeking algorithm without superiorization under identical conditions? (ii) How does LinSup fare in comparison with the Simplex method for solving linear programming problems? Based on our computational experiments presented here, the answers to these two questions are: ‘yes’ and ‘very well’, respectively.

Pathways to multidrug-resistant tuberculosis diagnosis and treatment initiation: a qualitative comparison of patients' experiences in the era of rapid molecular diagnostic tests.

PubMed

Naidoo, Pren; van Niekerk, Margaret; du Toit, Elizabeth; Beyers, Nulda; Leon, Natalie

2015-10-28

Although new molecular diagnostic tests such as GenoType MTBDRplus and Xpert® MTB/RIF have reduced multidrug-resistant tuberculosis (MDR-TB) treatment initiation times, patients' experiences of diagnosis and treatment initiation are not known. This study aimed to explore and compare MDR-TB patients' experiences of their diagnostic and treatment initiation pathway in GenoType MTBDRplus and Xpert® MTB/RIF-based diagnostic algorithms. The study was undertaken in Cape Town, South Africa where primary health-care services provided free TB diagnosis and treatment. A smear, culture and GenoType MTBDRplus diagnostic algorithm was used in 2010, with Xpert® MTB/RIF phased in from 2011-2013. Participants diagnosed in each algorithm at four facilities were purposively sampled, stratifying by age, gender and MDR-TB risk profiles. We conducted in-depth qualitative interviews using a semi-structured interview guide. Through constant comparative analysis we induced common and divergent themes related to symptom recognition, health-care access, testing for MDR-TB and treatment initiation within and between groups. Data were triangulated with clinical information and health visit data from a structured questionnaire. We identified both enablers and barriers to early MDR-TB diagnosis and treatment. Half the patients had previously been treated for TB; most recognised recurring symptoms and reported early health-seeking. Those who attributed symptoms to other causes delayed health-seeking. Perceptions of poor public sector services were prevalent and may have contributed both to deferred health-seeking and to patient's use of the private sector, contributing to delays. However, once on treatment, most patients expressed satisfaction with public sector care. Two patients in the Xpert® MTB/RIF-based algorithm exemplified its potential to reduce delays, commencing MDR-TB treatment within a week of their first health contact. However, most patients in both algorithms experienced substantial delays. Avoidable health system delays resulted from providers not testing for TB at initial health contact, non-adherence to testing algorithms, results not being available and failure to promptly recall patients with positive results. Whilst the introduction of rapid tests such as Xpert® MTB/RIF can expedite MDR-TB diagnosis and treatment initiation, the full benefits are unlikely to be realised without reducing delays in health-seeking and addressing the structural barriers present in the health-care system.

Parallel Optimization of Polynomials for Large-scale Problems in Stability and Control

NASA Astrophysics Data System (ADS)

Kamyar, Reza

In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems --- in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) --- whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers --- machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers. We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.

Development of a validation model for the defense meteorological satellite program's special sensor microwave imager

NASA Technical Reports Server (NTRS)

Swift, C. T.; Goodberlet, M. A.; Wilkerson, J. C.

1990-01-01

The Defence Meteorological Space Program's (DMSP) Special Sensor Microwave/Imager (SSM/I), an operational wind speed algorithm was developed. The algorithm is based on the D-matrix approach which seeks a linear relationship between measured SSM/I brightness temperatures and environmental parameters. D-matrix performance was validated by comparing algorithm derived wind speeds with near-simultaneous and co-located measurements made by off-shore ocean buoys. Other topics include error budget modeling, alternate wind speed algorithms, and D-matrix performance with one or more inoperative SSM/I channels.

Landscape Characterization of Arctic Ecosystems Using Data Mining Algorithms and Large Geospatial Datasets

NASA Astrophysics Data System (ADS)

Langford, Z. L.; Kumar, J.; Hoffman, F. M.

2015-12-01

Observations indicate that over the past several decades, landscape processes in the Arctic have been changing or intensifying. A dynamic Arctic landscape has the potential to alter ecosystems across a broad range of scales. Accurate characterization is useful to understand the properties and organization of the landscape, optimal sampling network design, measurement and process upscaling and to establish a landscape-based framework for multi-scale modeling of ecosystem processes. This study seeks to delineate the landscape at Seward Peninsula of Alaska into ecoregions using large volumes (terabytes) of high spatial resolution satellite remote-sensing data. Defining high-resolution ecoregion boundaries is difficult because many ecosystem processes in Arctic ecosystems occur at small local to regional scales, which are often resolved in by coarse resolution satellites (e.g., MODIS). We seek to use data-fusion techniques and data analytics algorithms applied to Phased Array type L-band Synthetic Aperture Radar (PALSAR), Interferometric Synthetic Aperture Radar (IFSAR), Satellite for Observation of Earth (SPOT), WorldView-2, WorldView-3, and QuickBird-2 to develop high-resolution (˜5m) ecoregion maps for multiple time periods. Traditional analysis methods and algorithms are insufficient for analyzing and synthesizing such large geospatial data sets, and those algorithms rarely scale out onto large distributed- memory parallel computer systems. We seek to develop computationally efficient algorithms and techniques using high-performance computing for characterization of Arctic landscapes. We will apply a variety of data analytics algorithms, such as cluster analysis, complex object-based image analysis (COBIA), and neural networks. We also propose to use representativeness analysis within the Seward Peninsula domain to determine optimal sampling locations for fine-scale measurements. This methodology should provide an initial framework for analyzing dynamic landscape trends in Arctic ecosystems, such as shrubification and disturbances, and integration of ecoregions into multi-scale models.

A new momentum management controller for the space station

NASA Technical Reports Server (NTRS)

Wie, B.; Byun, K. W.; Warren, V. W.

1988-01-01

A new approach to CMG (control moment gyro) momentum management and attitude control of the Space Station is developed. The control algorithm utilizes both the gravity-gradient and gyroscopic torques to seek torque equilibrium attitude in the presence of secular and cyclic disturbances. Depending upon mission requirements, either pitch attitude or pitch-axis CMG momentum can be held constant: yaw attitude and roll-axis CMG momentum can be held constant, while roll attitude and yaw-axis CMG momentum cannot be held constant. As a result, the overall attitude and CMG momentum oscillations caused by cyclic aero-dynamic disturbances are minimized. A state feedback controller with minimal computer storage requirement for gain scheduling is also developed. The overall closed-loop system is stable for + or - 30 percent inertia matrix variations and has more than + or - 10 dB and 45 deg stability margins in each loop.

Fundamental resource-allocating model in colleges and universities based on Immune Clone Algorithms

NASA Astrophysics Data System (ADS)

Ye, Mengdie

2017-05-01

In this thesis we will seek the combination of antibodies and antigens converted from the optimal course arrangement and make an analogy with Immune Clone Algorithms. According to the character of the Algorithms, we apply clone, clone gene and clone selection to arrange courses. Clone operator can combine evolutionary search and random search, global search and local search. By cloning and clone mutating candidate solutions, we can find the global optimal solution quickly.

Image Information Mining Utilizing Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

2002-01-01

The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Optimisation of confinement in a fusion reactor using a nonlinear turbulence model

NASA Astrophysics Data System (ADS)

Highcock, E. G.; Mandell, N. R.; Barnes, M.

2018-04-01

The confinement of heat in the core of a magnetic fusion reactor is optimised using a multidimensional optimisation algorithm. For the first time in such a study, the loss of heat due to turbulence is modelled at every stage using first-principles nonlinear simulations which accurately capture the turbulent cascade and large-scale zonal flows. The simulations utilise a novel approach, with gyrofluid treatment of the small-scale drift waves and gyrokinetic treatment of the large-scale zonal flows. A simple near-circular equilibrium with standard parameters is chosen as the initial condition. The figure of merit, fusion power per unit volume, is calculated, and then two control parameters, the elongation and triangularity of the outer flux surface, are varied, with the algorithm seeking to optimise the chosen figure of merit. A twofold increase in the plasma power per unit volume is achieved by moving to higher elongation and strongly negative triangularity.

Simulation Experiments with Goal-Seeking Adaptive Elements.

DTIC Science & Technology

1984-02-01

when it comes to cognition and particularly bad when it comes to remote sensing, goal seeking, adaptation and decision making, where brains excel. In...Erlbaum 1981, 161-187 Hinton, G. E., & Sejnowski, T. J. Analyzing Cooperative Computation. Proceedings of the Fifth Annual Conference of the Cognitive ...Algorithms and Applications. Springer-Verlag, 1981. Lenat, D. B., Hayes-Roth, F., Klahr, P. Cognitive economy. Stanford Heuristic Program- ming Project HPP

11.2 YIP Human In the Loop Statistical RelationalLearners

DTIC Science & Technology

2017-10-23

learning formalisms including inverse reinforcement learning [4] and statistical relational learning [7, 5, 8]. We have also applied our algorithms in...one introduced for label preferences. 4 Figure 2: Active Advice Seeking for Inverse Reinforcement Learning. active advice seeking is in selecting the...learning tasks. 1.2.1 Sequential Decision-Making Our previous work on advice for inverse reinforcement learning (IRL) defined advice as action

Quadratic Programming for Allocating Control Effort

NASA Technical Reports Server (NTRS)

Singh, Gurkirpal

2005-01-01

A computer program calculates an optimal allocation of control effort in a system that includes redundant control actuators. The program implements an iterative (but otherwise single-stage) algorithm of the quadratic-programming type. In general, in the quadratic-programming problem, one seeks the values of a set of variables that minimize a quadratic cost function, subject to a set of linear equality and inequality constraints. In this program, the cost function combines control effort (typically quantified in terms of energy or fuel consumed) and control residuals (differences between commanded and sensed values of variables to be controlled). In comparison with prior control-allocation software, this program offers approximately equal accuracy but much greater computational efficiency. In addition, this program offers flexibility, robustness to actuation failures, and a capability for selective enforcement of control requirements. The computational efficiency of this program makes it suitable for such complex, real-time applications as controlling redundant aircraft actuators or redundant spacecraft thrusters. The program is written in the C language for execution in a UNIX operating system.

Performance seeking control: Program overview and future directions

NASA Technical Reports Server (NTRS)

Gilyard, Glenn B.; Orme, John S.

1993-01-01

A flight test evaluation of the performance-seeking control (PSC) algorithm on the NASA F-15 highly integrated digital electronic control research aircraft was conducted for single-engine operation at subsonic and supersonic speeds. The model-based PSC system was developed with three optimization modes: minimum fuel flow at constant thrust, minimum turbine temperature at constant thrust, and maximum thrust at maximum dry and full afterburner throttle settings. Subsonic and supersonic flight testing were conducted at the NASA Dryden Flight Research Facility covering the three PSC optimization modes and over the full throttle range. Flight results show substantial benefits. In the maximum thrust mode, thrust increased up to 15 percent at subsonic and 10 percent at supersonic flight conditions. The minimum fan turbine inlet temperature mode reduced temperatures by more than 100 F at high altitudes. The minimum fuel flow mode results decreased fuel consumption up to 2 percent in the subsonic regime and almost 10 percent supersonically. These results demonstrate that PSC technology can benefit the next generation of fighter or transport aircraft. NASA Dryden is developing an adaptive aircraft performance technology system that is measurement based and uses feedback to ensure optimality. This program will address the technical weaknesses identified in the PSC program and will increase performance gains.

A multiple objective optimization approach to quality control

NASA Technical Reports Server (NTRS)

Seaman, Christopher Michael

1991-01-01

The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios: tuning of process controllers to meet specified performance objectives and tuning of process inputs to meet specified quality objectives. Five case studies are presented.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Extremum seeking-based optimization of high voltage converter modulator rise-time

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheinker, Alexander; Bland, Michael; Krstic, Miroslav

2013-02-01

We digitally implement an extremum seeking (ES) algorithm, which optimizes the rise time of the output voltage of a high voltage converter modulator (HVCM) at the Los Alamos Neutron Science Center (LANSCE) HVCM test stand by iteratively, simultaneously tuning the first 8 switching edges of each of the three phase drive waveforms (24 variables total). We achieve a 50 μs rise time, which is reduction in half compared to the 100 μs achieved at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. Considering that HVCMs typically operate with an output voltage of 100 kV, with a 60Hz repetitionmore » rate, the 50 μs rise time reduction will result in very significant energy savings. The ES algorithm will prove successful, despite the noisy measurements and cost calculations, confirming the theoretical results that the algorithm is not affected by noise whose frequency components are independent of the perturbing frequencies.« less

Intelligent energy harvesting scheme for microbial fuel cells: Maximum power point tracking and voltage overshoot avoidance

NASA Astrophysics Data System (ADS)

Alaraj, Muhannad; Radenkovic, Miloje; Park, Jae-Do

2017-02-01

Microbial fuel cells (MFCs) are renewable and sustainable energy sources that can be used for various applications. The MFC output power depends on its biochemical conditions as well as the terminal operating points in terms of output voltage and current. There exists one operating point that gives the maximum possible power from the MFC, maximum power point (MPP), for a given operating condition. However, this MPP may vary and needs to be tracked in order to maintain the maximum power extraction from the MFC. Furthermore, MFC reactors often develop voltage overshoots that cause drastic drops in the terminal voltage, current, and the output power. When the voltage overshoot happens, an additional control measure is necessary as conventional MPPT algorithms will fail because of the change in the voltage-current relationship. In this paper, the extremum seeking (ES) algorithm was used to track the varying MPP and a voltage overshoot avoidance (VOA) algorithm is developed to manage the voltage overshoot conditions. The proposed ES-MPPT with VOA algorithm was able to extract 197.2 mJ during 10-min operation avoiding voltage overshoot, while the ES MPPT-only scheme stopped harvesting after only 18.75 mJ because of the voltage overshoot happened at 0.4 min.

Making predictions in a changing world-inference, uncertainty, and learning.

PubMed

O'Reilly, Jill X

2013-01-01

To function effectively, brains need to make predictions about their environment based on past experience, i.e., they need to learn about their environment. The algorithms by which learning occurs are of interest to neuroscientists, both in their own right (because they exist in the brain) and as a tool to model participants' incomplete knowledge of task parameters and hence, to better understand their behavior. This review focusses on a particular challenge for learning algorithms-how to match the rate at which they learn to the rate of change in the environment, so that they use as much observed data as possible whilst disregarding irrelevant, old observations. To do this algorithms must evaluate whether the environment is changing. We discuss the concepts of likelihood, priors and transition functions, and how these relate to change detection. We review expected and estimation uncertainty, and how these relate to change detection and learning rate. Finally, we consider the neural correlates of uncertainty and learning. We argue that the neural correlates of uncertainty bear a resemblance to neural systems that are active when agents actively explore their environments, suggesting that the mechanisms by which the rate of learning is set may be subject to top down control (in circumstances when agents actively seek new information) as well as bottom up control (by observations that imply change in the environment).

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Computing Game-Theoretic Solutions for Security in the Medium Term

DTIC Science & Technology

This project concerns the design of algorithms for computing game- theoretic solutions . (Game theory concerns how to act in a strategically optimal...way in environments with other agents who also seek to act optimally but have different , and possibly opposite, interests .) Such algorithms have...recently found application in a number of real-world security applications, including among others airport security, scheduling Federal Air Marshals, and

Guidance, Navigation, and Control Technology Assessment for Future Planetary Science Missions

NASA Technical Reports Server (NTRS)

Beauchamp, Pat; Cutts, James; Quadrelli, Marco B.; Wood, Lincoln J.; Riedel, Joseph E.; McHenry, Mike; Aung, MiMi; Cangahuala, Laureano A.; Volpe, Rich

2013-01-01

Future planetary explorations envisioned by the National Research Council's (NRC's) report titled Vision and Voyages for Planetary Science in the Decade 2013-2022, developed for NASA Science Mission Directorate (SMD) Planetary Science Division (PSD), seek to reach targets of broad scientific interest across the solar system. This goal requires new capabilities such as innovative interplanetary trajectories, precision landing, operation in close proximity to targets, precision pointing, multiple collaborating spacecraft, multiple target tours, and advanced robotic surface exploration. Advancements in Guidance, Navigation, and Control (GN&C) and Mission Design in the areas of software, algorithm development and sensors will be necessary to accomplish these future missions. This paper summarizes the key GN&C and mission design capabilities and technologies needed for future missions pursuing SMD PSD's scientific goals.

Integration of energy management concepts into the flight deck

NASA Technical Reports Server (NTRS)

Morello, S. A.

1981-01-01

The rapid rise of fuel costs has become a major concern of the commercial aviation industry, and it has become mandatory to seek means by which to conserve fuel. A research program was initiated in 1979 to investigate the integration of fuel-conservative energy/flight management computations and information into today's and tomorrow's flight deck. One completed effort within this program has been the development and flight testing of a fuel-efficient, time-based metering descent algorithm in a research cockpit environment. Research flights have demonstrated that time guidance and control in the cockpit was acceptable to both pilots and ATC controllers. Proper descent planning and energy management can save fuel for the individual aircraft as well as the fleet by helping to maintain a regularized flow into the terminal area.

Deterministic implementations of single-photon multi-qubit Deutsch-Jozsa algorithms with linear optics

NASA Astrophysics Data System (ADS)

Wei, Hai-Rui; Liu, Ji-Zhen

2017-02-01

It is very important to seek an efficient and robust quantum algorithm demanding less quantum resources. We propose one-photon three-qubit original and refined Deutsch-Jozsa algorithms with polarization and two linear momentums degrees of freedom (DOFs). Our schemes are constructed by solely using linear optics. Compared to the traditional ones with one DOF, our schemes are more economic and robust because the necessary photons are reduced from three to one. Our linear-optic schemes are working in a determinate way, and they are feasible with current experimental technology.

Deterministic implementations of single-photon multi-qubit Deutsch–Jozsa algorithms with linear optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Hai-Rui, E-mail: hrwei@ustb.edu.cn; Liu, Ji-Zhen

2017-02-15

It is very important to seek an efficient and robust quantum algorithm demanding less quantum resources. We propose one-photon three-qubit original and refined Deutsch–Jozsa algorithms with polarization and two linear momentums degrees of freedom (DOFs). Our schemes are constructed by solely using linear optics. Compared to the traditional ones with one DOF, our schemes are more economic and robust because the necessary photons are reduced from three to one. Our linear-optic schemes are working in a determinate way, and they are feasible with current experimental technology.

Integrating Predictive Modeling with Control System Design for Managed Aquifer Recharge and Recovery Applications

NASA Astrophysics Data System (ADS)

Drumheller, Z. W.; Regnery, J.; Lee, J. H.; Illangasekare, T. H.; Kitanidis, P. K.; Smits, K. M.

2014-12-01

Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization led to reduced natural recharge rates and overuse. Scientists and engineers have begun to re-investigate the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. MAR systems offer the possibility of naturally increasing groundwater storage while improving the quality of impaired water used for recharge. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. Our project seeks to ease the operational challenges of MAR facilities through the implementation of active sensor networks, adaptively calibrated flow and transport models, and simulation-based meta-heuristic control optimization methods. The developed system works by continually collecting hydraulic and water quality data from a sensor network embedded within the aquifer. The data is fed into an inversion algorithm, which calibrates the parameters and initial conditions of a predictive flow and transport model. The calibrated model is passed to a meta-heuristic control optimization algorithm (e.g. genetic algorithm) to execute the simulations and determine the best course of action, i.e., the optimal pumping policy for current aquifer conditions. The optimal pumping policy is manually or autonomously applied. During operation, sensor data are used to assess the accuracy of the optimal prediction and augment the pumping strategy as needed. At laboratory-scale, a small (18"H x 46"L) and an intermediate (6'H x 16'L) two-dimensional synthetic aquifer were constructed and outfitted with sensor networks. Data collection and model inversion components were developed and sensor data were validated by analytical measurements.

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

NASA Astrophysics Data System (ADS)

Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

2014-05-01

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

NASA Technical Reports Server (NTRS)

Riedel, Morris; Ramachandran, Rahul; Baumann, Peter

2014-01-01

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

NASA Technical Reports Server (NTRS)

Kocurek, Michael J.

2005-01-01

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

What kind of computation is intelligence. A framework for integrating different kinds of expertise

NASA Technical Reports Server (NTRS)

Chandrasekaran, B.

1989-01-01

The view that the deliberative aspect of intelligent behavior is a distinct type of algorithm; in particular, a goal-seeking exploratory process using qualitative representations of knowledge and inference is elaborated. There are other kinds of algorithms that also embody expertise in domains. The different types of expertise and how they can and should be integrated to give full account of expert behavior are discussed.

A ’Multiple Pivoting’ Algorithm for Goal-Interval Programming Formulations.

DTIC Science & Technology

1980-03-01

jotso _P- ,- Research Report CCS 355 A "MULTIPLE PIVOTING" ALGORITHM FOR GOAL-INTERVAL PROGRAMMING FORMULATIONS by R. Armstrong* A. Charnes*W. Cook...J. Godfrey*** March 1980 *The University of Texas at Austin **York University, Downsview, Ontario, Canada ***Washington, DC This research was partly...areas. However, the main direction of goal programing research has been in formulating models instead of seeking procedures that would provide

Information fusion performance evaluation for motion imagery data using mutual information: initial study

NASA Astrophysics Data System (ADS)

Grieggs, Samuel M.; McLaughlin, Michael J.; Ezekiel, Soundararajan; Blasch, Erik

2015-06-01

As technology and internet use grows at an exponential rate, video and imagery data is becoming increasingly important. Various techniques such as Wide Area Motion imagery (WAMI), Full Motion Video (FMV), and Hyperspectral Imaging (HSI) are used to collect motion data and extract relevant information. Detecting and identifying a particular object in imagery data is an important step in understanding visual imagery, such as content-based image retrieval (CBIR). Imagery data is segmented and automatically analyzed and stored in dynamic and robust database. In our system, we seek utilize image fusion methods which require quality metrics. Many Image Fusion (IF) algorithms have been proposed based on different, but only a few metrics, used to evaluate the performance of these algorithms. In this paper, we seek a robust, objective metric to evaluate the performance of IF algorithms which compares the outcome of a given algorithm to ground truth and reports several types of errors. Given the ground truth of a motion imagery data, it will compute detection failure, false alarm, precision and recall metrics, background and foreground regions statistics, as well as split and merge of foreground regions. Using the Structural Similarity Index (SSIM), Mutual Information (MI), and entropy metrics; experimental results demonstrate the effectiveness of the proposed methodology for object detection, activity exploitation, and CBIR.

On Social Optima of Non-Cooperative Mean Field Games

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Zhao, Lin

This paper studies the social optima in noncooperative mean-field games for a large population of agents with heterogeneous stochastic dynamic systems. Each agent seeks to maximize an individual utility functional, and utility functionals of different agents are coupled through a mean field term that depends on the mean of the population states/controls. The paper has the following contributions. First, we derive a set of control strategies for the agents that possess *-Nash equilibrium property, and converge to the mean-field Nash equilibrium as the population size goes to infinity. Second, we study the social optimal in the mean field game. Wemore » derive the conditions, termed the socially optimal conditions, under which the *-Nash equilibrium of the mean field game maximizes the social welfare. Third, a primal-dual algorithm is proposed to compute the *-Nash equilibrium of the mean field game. Since the *-Nash equilibrium of the mean field game is socially optimal, we can compute the equilibrium by solving the social welfare maximization problem, which can be addressed by a decentralized primal-dual algorithm. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach.« less

Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan

PubMed Central

Palmer, Jennifer J.; Surur, Elizeous I.; Goch, Garang W.; Mayen, Mangar A.; Lindner, Andreas K.; Pittet, Anne; Kasparian, Serena; Checchi, Francesco; Whitty, Christopher J. M.

2013-01-01

Background Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. Methodology/Principal Findings Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. Conclusions/Significance In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be validated elsewhere. PMID:23350005

Ideas for Future GPS Timing Improvements

NASA Technical Reports Server (NTRS)

Hutsell, Steven T.

1996-01-01

Having recently met stringent criteria for full operational capability (FOC) certification, the Global Positioning System (GPS) now has higher customer expectations than ever before. In order to maintain customer satisfaction, and the meet the even high customer demands of the future, the GPS Master Control Station (MCS) must play a critical role in the process of carefully refining the performance and integrity of the GPS constellation, particularly in the area of timing. This paper will present an operational perspective on several ideas for improving timing in GPS. These ideas include the desire for improving MCS - US Naval Observatory (USNO) data connectivity, an improved GPS-Coordinated Universal Time (UTC) prediction algorithm, a more robust Kalman Filter, and more features in the GPS reference time algorithm (the GPS composite clock), including frequency step resolution, a more explicit use of the basic time scale equation, and dynamic clock weighting. Current MCS software meets the exceptional challenge of managing an extremely complex constellation of 24 navigation satellites. The GPS community will, however, always seek to improve upon this performance and integrity.

Risk-Seeking Versus Risk-Avoiding Investments in Noisy Periodic Environments

NASA Astrophysics Data System (ADS)

Navarro-Barrientos, J. Emeterio; Walter, Frank E.; Schweitzer, Frank

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, x(t), and at each time step invest a particular fraction, q(t), of their budget. The return on investment (RoI), r(t), is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction q(t) proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value qmax if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future r(t), dependent on their internal complexity. Here, we compare "zero-intelligent" agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict r(t). The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.

Efficient bulk-loading of gridfiles

NASA Technical Reports Server (NTRS)

Leutenegger, Scott T.; Nicol, David M.

1994-01-01

This paper considers the problem of bulk-loading large data sets for the gridfile multiattribute indexing technique. We propose a rectilinear partitioning algorithm that heuristically seeks to minimize the size of the gridfile needed to ensure no bucket overflows. Empirical studies on both synthetic data sets and on data sets drawn from computational fluid dynamics applications demonstrate that our algorithm is very efficient, and is able to handle large data sets. In addition, we present an algorithm for bulk-loading data sets too large to fit in main memory. Utilizing a sort of the entire data set it creates a gridfile without incurring any overflows.

Action Recommendation for Cyber Resilience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choudhury, Sutanay; Rodriguez, Luke R.; Curtis, Darren S.

2015-09-01

This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems.

Voltage oriented control of self-excited induction generator for wind energy system with MPPT

NASA Astrophysics Data System (ADS)

Amieur, Toufik; Taibi, Djamel; Amieur, Oualid

2018-05-01

This paper presents the study and simulation of the self-excited induction generator in the wind power production in isolated sites. With this intention, a model of the wind turbine was established. Extremum-seeking control algorithm method by using Maximum Power Point Tracking (MPPT) is proposed control solution aims at driving the average position of the operating point near to optimality. The reference of turbine rotor speed is adjusted such that the turbine operates around maximum power for the current wind speed value. After a brief review of the concepts of converting wind energy into electrical energy. The proposed modeling tools were developed to study the performance of standalone induction generators connected to capacitor bank. The purpose of this technique is to maintain a constant voltage at the output of the rectifier whatever the loads and speeds. The system studied in this work is developed and tested in MATLAB/Simulink environment. Simulation results validate the performance and effectiveness of the proposed control methods.

A bio-inspired glucose controller based on pancreatic β-cell physiology.

PubMed

Herrero, Pau; Georgiou, Pantelis; Oliver, Nick; Johnston, Desmond G; Toumazou, Christofer

2012-05-01

Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas. A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller. Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents. This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model. © 2012 Diabetes Technology Society.

A Bio-Inspired Glucose Controller Based on Pancreatic β-Cell Physiology

PubMed Central

Herrero, Pau; Georgiou, Pantelis; Oliver, Nick; Johnston, Desmond G; Toumazou, Christofer

2012-01-01

Introduction Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas. Methods A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller. Results Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents. Conclusions This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model. PMID:22768892

The Heterogeneity of Children of Alcoholics: Emotional Needs and Help-Seeking Propensity.

ERIC Educational Resources Information Center

Hinson, Renee C.; And Others

1993-01-01

Examined parental alcoholism and help-seeking behavior in college students classified as children of alcoholics (COAs, n=83), Help-seeking COAs (n=51), Controls (n=86), and Help-seeking Controls (n=90). Findings revealed that help-seeking appeared to be the more significant variable for discriminating differences in emotional needs of college…

Inverse Trilateration - A Positional Goal Seeking Algorithm for a Swarm of Autonomous Drones

DTIC Science & Technology

2014-06-12

uny ouc or the n clrmus, \\Vt: ca11 •lcvc lop a system of equations for that ptnticular drmu .. ’- thc onn which will IJc att.<~mpting t.o fi11d u...a111l thc at’Tial vchich:. One sc:euerio for f’lctcnuiuing clistaocc to an nbject of unkuown location is to co1is11lcr a trauSJnith’r of lowvm powcr1...U1ing On6owU V"ual ond foertiol SvuinJ. In rroc. or the \\\\Orbhop Oh Visual Control of Mobilr Robols (VlC4’>.MoR) at thc - JEEE/RJS lntrmaiion"I Confl

Reinforcement Learning for Constrained Energy Trading Games With Incomplete Information.

PubMed

Wang, Huiwei; Huang, Tingwen; Liao, Xiaofeng; Abu-Rub, Haitham; Chen, Guo

2017-10-01

This paper considers the problem of designing adaptive learning algorithms to seek the Nash equilibrium (NE) of the constrained energy trading game among individually strategic players with incomplete information. In this game, each player uses the learning automaton scheme to generate the action probability distribution based on his/her private information for maximizing his own averaged utility. It is shown that if one of admissible mixed-strategies converges to the NE with probability one, then the averaged utility and trading quantity almost surely converge to their expected ones, respectively. For the given discontinuous pricing function, the utility function has already been proved to be upper semicontinuous and payoff secure which guarantee the existence of the mixed-strategy NE. By the strict diagonal concavity of the regularized Lagrange function, the uniqueness of NE is also guaranteed. Finally, an adaptive learning algorithm is provided to generate the strategy probability distribution for seeking the mixed-strategy NE.

Feasibility of web-based self-triage by parents of children with influenza-like illness: a cautionary tale.

PubMed

Anhang Price, Rebecca; Fagbuyi, Daniel; Harris, Racine; Hanfling, Dan; Place, Frederick; Taylor, Todd B; Kellermann, Arthur L

2013-02-01

Self-triage using web-based decision support could be a useful way to encourage appropriate care-seeking behavior and reduce health system surge in epidemics. However, the feasibility and safety of this strategy have not previously been evaluated. To assess the usability and safety of Strategy for Off-site Rapid Triage (SORT) for Kids, a web-based decision support tool designed to translate clinical guidance developed by the Centers for Disease Control and Prevention to help parents and adult caregivers determine if a child with influenza-like illness requires immediate care in an emergency department (ED). Prospective pilot validation study conducted between February 8 and April 30, 2012. Staff who abstracted medical records and made follow-up calls were blinded to the SORT algorithm's assessment of the child's level of risk. Two pediatric emergency departments in the National Capital Region. Convenience sample of 294 parents and adult caregivers who were at least 18 years of age; able to read and speak English; and the parent or legal guardian of a child 18 years or younger presenting to 1 of 2 EDs with signs and symptoms meeting Centers for Disease Control and Prevention criteria for influenza-like illness. Completion of the SORT for Kids survey. Caregiver ratings of the website's usability and the sensitivity of the underlying algorithm for identifying children who required immediate ED management of influenza-like illness, defined as receipt of 1 or more of 5 essential clinical services. Ninety percent of participants reported that the website was "very easy" to understand and use. Ratings did not differ by respondent race, ethnicity, or educational attainment. Of the 15 patients whose initial ED visit met explicit criteria for clinical necessity, the Centers for Disease Control and Prevention algorithm classified 14 as high risk, resulting in an overall sensitivity of 93.3% (exact 95% CI, 68.1%-99.8%). Specificity of the algorithm was poor. This pilot study suggests that web-based decision support to help parents and adult caregivers self-triage children with influenza-like illness is feasible. However, prospective refinement of the clinical algorithm is needed to improve its specificity without compromising patient safety.

Optimal Design of Gradient Materials and Bi-Level Optimization of Topology Using Targets (BOTT)

NASA Astrophysics Data System (ADS)

Garland, Anthony

The objective of this research is to understand the fundamental relationships necessary to develop a method to optimize both the topology and the internal gradient material distribution of a single object while meeting constraints and conflicting objectives. Functionally gradient material (FGM) objects possess continuous varying material properties throughout the object, and they allow an engineer to tailor individual regions of an object to have specific mechanical properties by locally modifying the internal material composition. A variety of techniques exists for topology optimization, and several methods exist for FGM optimization, but combining the two together is difficult. Understanding the relationship between topology and material gradient optimization enables the selection of an appropriate model and the development of algorithms, which allow engineers to design high-performance parts that better meet design objectives than optimized homogeneous material objects. For this research effort, topology optimization means finding the optimal connected structure with an optimal shape. FGM optimization means finding the optimal macroscopic material properties within an object. Tailoring the material constitutive matrix as a function of position results in gradient properties. Once, the target macroscopic properties are known, a mesostructure or a particular material nanostructure can be found which gives the target material properties at each macroscopic point. This research demonstrates that topology and gradient materials can both be optimized together for a single part. The algorithms use a discretized model of the domain and gradient based optimization algorithms. In addition, when considering two conflicting objectives the algorithms in this research generate clear 'features' within a single part. This tailoring of material properties within different areas of a single part (automated design of 'features') using computational design tools is a novel benefit of gradient material designs. A macroscopic gradient can be achieved by varying the microstructure or the mesostructures of an object. The mesostructure interpretation allows for more design freedom since the mesostructures can be tuned to have non-isotropic material properties. A new algorithm called Bi-level Optimization of Topology using Targets (BOTT) seeks to find the best distribution of mesostructure designs throughout a single object in order to minimize an objective value. On the macro level, the BOTT algorithm optimizes the macro topology and gradient material properties within the object. The BOTT algorithm optimizes the material gradient by finding the best constitutive matrix at each location with the object. In order to enhance the likelihood that a mesostructure can be generated with the same equivalent constitutive matrix, the variability of the constitutive matrix is constrained to be an orthotropic material. The stiffness in the X and Y directions (of the base coordinate system) can change in addition to rotating the orthotropic material to align with the loading at each region. Second, the BOTT algorithm designs mesostructures with macroscopic properties equal to the target properties found in step one while at the same time the algorithm seeks to minimize material usage in each mesostructure. The mesostructure algorithm maximizes the strain energy of the mesostructures unit cell when a pseudo strain is applied to the cell. A set of experiments reveals the fundamental relationship between target cell density and the strain (or pseudo strain) applied to a unit cell and the output effective properties of the mesostructure. At low density, a few mesostructure unit cell design are possible, while at higher density the mesostructure unit cell designs have many possibilities. Therefore, at low densities the effective properties of the mesostructure are a step function of the applied pseudo strain. At high densities, the effective properties of the mesostructure are continuous function of the applied pseudo strain. Finally, the macro and mesostructure designs are coordinated so that the macro and meso levels agree on the material properties at each macro region. In addition, a coordination effort seeks to coordinate the boundaries of adjacent mesostructure designs so that the macro load path is transmitted from one mesostructure design to its neighbors. The BOTT algorithm has several advantages over existing algorithms within the literature. First, the BOTT algorithm significantly reduces the computational power required to run the algorithm. Second, the BOTT algorithm indirectly enforces a minimum mesostructure density constraint which increases the manufacturability of the final design. Third, the BOTT algorithm seeks to transfer the load from one mesostructure to its neighbors by coordinating the boundaries of adjacent mesostructure designs. However, the BOTT algorithm can still be improved since it may have difficulty converging due to the step function nature of the mesostructure design problem at low density.

An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

NASA Astrophysics Data System (ADS)

Li, Si-Yu; Wang, Shu-Ming; Wang, Peng-Fei; Su, Xiao-Lu; Zhang, Xin-Song; Dong, Zhi-Hui

2018-05-01

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

Kernel Temporal Differences for Neural Decoding

PubMed Central

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

SSM/I Rainfall Volume Correlated with Deepening Rate in Extratropical Cyclones

NASA Technical Reports Server (NTRS)

Petty, Grant W.; Miller, Douglas K.

1994-01-01

With the emergence of reasonably robust, physically based rain rate algorithms designed for the Special Sensor Microwave/Imager (SSM/I), a unique opportunity exists to directly observe a physical component which can contribute to or be a signature of cyclone deepening (latent heat release). The emphasis of the research in this paper is to seek systematic differences in rain rate observed by the SSM/I, using the algorithm of Petty in cases of explosive and nonexplosive cyclone deepening.

Advanced Interactive Display Formats for Terminal Area Traffic Control

NASA Technical Reports Server (NTRS)

Grunwald, Arthur J.; Shaviv, G. E.

1999-01-01

This research project deals with an on-line dynamic method for automated viewing parameter management in perspective displays. Perspective images are optimized such that a human observer will perceive relevant spatial geometrical features with minimal errors. In order to compute the errors at which observers reconstruct spatial features from perspective images, a visual spatial-perception model was formulated. The model was employed as the basis of an optimization scheme aimed at seeking the optimal projection parameter setting. These ideas are implemented in the context of an air traffic control (ATC) application. A concept, referred to as an active display system, was developed. This system uses heuristic rules to identify relevant geometrical features of the three-dimensional air traffic situation. Agile, on-line optimization was achieved by a specially developed and custom-tailored genetic algorithm (GA), which was to deal with the multi-modal characteristics of the objective function and exploit its time-evolving nature.

Compensation in the presence of deep turbulence using tiled-aperture architectures

NASA Astrophysics Data System (ADS)

Spencer, Mark F.; Brennan, Terry J.

2017-05-01

The presence of distributed-volume atmospheric aberrations or "deep turbulence" presents unique challenges for beam-control applications which look to sense and correct for disturbances found along the laser-propagation path. This paper explores the potential for branch-point-tolerant reconstruction algorithms and tiled-aperture architectures to correct for the branch cuts contained in the phase function due to deep-turbulence conditions. Using wave-optics simulations, the analysis aims to parameterize the fitting-error performance of tiled-aperture architectures operating in a null-seeking control loop with piston, tip, and tilt compensation of the individual optical beamlet trains. To evaluate fitting-error performance, the analysis plots normalized power in the bucket as a function of the Fried coherence diameter, the log-amplitude variance, and the number of subapertures for comparison purposes. Initial results show that tiled-aperture architectures with a large number of subapertures outperform filled-aperture architectures with continuous-face-sheet deformable mirrors.

Electric Motors Maintenance Planning From Its Operating Variables

NASA Astrophysics Data System (ADS)

Rodrigues, Francisco; Fonseca, Inácio; Farinha, José Torres; Ferreira, Luís; Galar, Diego

2017-09-01

The maintenance planning corresponds to an approach that seeks to maximize the availability of equipment and, consequently, increase the levels of competitiveness of companies by increasing production times. This paper presents a maintenance planning based on operating variables (number of hours worked, duty cycles, number of revolutions) to maximizing the availability of operation of electrical motors. The reading of the operating variables and its sampling is done based on predetermined sampling cycles and subsequently is made the data analysis through time series algorithms aiming to launch work orders before reaching the variables limit values. This approach is supported by tools and technologies such as logical applications that enable a graphical user interface for access to relevant information about their Physical Asset HMI (Human Machine Interface), including the control and supervision by acquisition through SCADA (Supervisory Control And data acquisition) data, also including the communication protocols among different logical applications.

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

Drosophila mushroom bodies integrate hunger and satiety signals to control innate food-seeking behavior.

PubMed

Tsao, Chang-Hui; Chen, Chien-Chun; Lin, Chen-Han; Yang, Hao-Yu; Lin, Suewei

2018-03-16

The fruit fly can evaluate its energy state and decide whether to pursue food-related cues. Here, we reveal that the mushroom body (MB) integrates hunger and satiety signals to control food-seeking behavior. We have discovered five pathways in the MB essential for hungry flies to locate and approach food. Blocking the MB-intrinsic Kenyon cells (KCs) and the MB output neurons (MBONs) in these pathways impairs food-seeking behavior. Starvation bi-directionally modulates MBON responses to a food odor, suggesting that hunger and satiety controls occur at the KC-to-MBON synapses. These controls are mediated by six types of dopaminergic neurons (DANs). By manipulating these DANs, we could inhibit food-seeking behavior in hungry flies or promote food seeking in fed flies. Finally, we show that the DANs potentially receive multiple inputs of hunger and satiety signals. This work demonstrates an information-rich central circuit in the fly brain that controls hunger-driven food-seeking behavior. © 2018, Tsao et al.

Drosophila mushroom bodies integrate hunger and satiety signals to control innate food-seeking behavior

PubMed Central

Tsao, Chang-Hui; Chen, Chien-Chun; Lin, Chen-Han; Yang, Hao-Yu

2018-01-01

The fruit fly can evaluate its energy state and decide whether to pursue food-related cues. Here, we reveal that the mushroom body (MB) integrates hunger and satiety signals to control food-seeking behavior. We have discovered five pathways in the MB essential for hungry flies to locate and approach food. Blocking the MB-intrinsic Kenyon cells (KCs) and the MB output neurons (MBONs) in these pathways impairs food-seeking behavior. Starvation bi-directionally modulates MBON responses to a food odor, suggesting that hunger and satiety controls occur at the KC-to-MBON synapses. These controls are mediated by six types of dopaminergic neurons (DANs). By manipulating these DANs, we could inhibit food-seeking behavior in hungry flies or promote food seeking in fed flies. Finally, we show that the DANs potentially receive multiple inputs of hunger and satiety signals. This work demonstrates an information-rich central circuit in the fly brain that controls hunger-driven food-seeking behavior. PMID:29547121

An efficient algorithm for pairwise local alignment of protein interaction networks

DOE PAGES

Chen, Wenbin; Schmidt, Matthew; Tian, Wenhong; ...

2015-04-01

Recently, researchers seeking to understand, modify, and create beneficial traits in organisms have looked for evolutionarily conserved patterns of protein interactions. Their conservation likely means that the proteins of these conserved functional modules are important to the trait's expression. In this paper, we formulate the problem of identifying these conserved patterns as a graph optimization problem, and develop a fast heuristic algorithm for this problem. We compare the performance of our network alignment algorithm to that of the MaWISh algorithm [Koyuturk M, Kim Y, Topkara U, Subramaniam S, Szpankowski W, Grama A, Pairwise alignment of protein interaction networks, J Computmore » Biol 13(2): 182-199, 2006.], which bases its search algorithm on a related decision problem formulation. We find that our algorithm discovers conserved modules with a larger number of proteins in an order of magnitude less time. In conclusion, the protein sets found by our algorithm correspond to known conserved functional modules at comparable precision and recall rates as those produced by the MaWISh algorithm.« less

Sex differences in the developmental trajectories of impulse control and sensation-seeking from early adolescence to early adulthood.

PubMed

Shulman, Elizabeth P; Harden, K Paige; Chein, Jason M; Steinberg, Laurence

2015-01-01

It has been proposed that high rates of risk-taking in adolescence are partly attributable to patterns of neurobiological development that promote an increase in sensation-seeking tendencies at a time when impulse control is still developing. It is not known, however, whether this pattern is the same for males and females. The present study investigates sex differences in the developmental trajectories of self-reported impulse control and sensation-seeking between the ages of 10 and 25 using longitudinal data from the National Longitudinal Study of Youth 1979 Child and Young Adult Survey (N = 8,270; 49% female; 33% Black, 22% Hispanic, 45% Non-Black, Non-Hispanic). Prior work has found that, consistent with the dual-systems model of adolescent neurobiological development, sensation-seeking rises and falls across this age span, whereas impulse control increases into the 20s. In the present study, we find that this same general pattern holds for both males and females, but with some key differences. As expected, males exhibit higher levels of sensation-seeking and lower levels of impulse control than females. Differences also emerged in the shapes of the developmental trajectories. Females reach peak levels of sensation-seeking earlier than males (consistent with the idea that sensation-seeking is linked to pubertal development) and decline in sensation-seeking more rapidly thereafter. Also, males increase in impulse control more gradually than females. Consequently, sex differences in both impulse control and sensation-seeking increase with age. The findings suggest that the window of heightened vulnerability to risk-taking during adolescence may be greater in magnitude and more protracted for males than for females.

An intelligent value-driven scheduling system for Space Station Freedom with special emphasis on the electric power system

NASA Technical Reports Server (NTRS)

Krupp, Joseph C.

1991-01-01

The Electric Power Control System (EPCS) created by Decision-Science Applications, Inc. (DSA) for the Lewis Research Center is discussed. This system makes decisions on what to schedule and when to schedule it, including making choices among various options or ways of performing a task. The system is goal-directed and seeks to shape resource usage in an optimal manner using a value-driven approach. Discussed here are considerations governing what makes a good schedule, how to design a value function to find the best schedule, and how to design the algorithm that finds the schedule that maximizes this value function. Results are shown which demonstrate the usefulness of the techniques employed.

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)

PubMed Central

Li, Isaac TS; Shum, Warren; Truong, Kevin

2007-01-01

Background To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. Results In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. Conclusion This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching. PMID:17555593

160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA).

PubMed

Li, Isaac T S; Shum, Warren; Truong, Kevin

2007-06-07

To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive. In this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor. This design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems.

PubMed

Shabanzadeh, Parvaneh; Yusof, Rubiyah

2015-01-01

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

Flexible Wing Base Micro Aerial Vehicles: Vision-Guided Flight Stability and Autonomy for Micro Air Vehicles

NASA Technical Reports Server (NTRS)

Ettinger, Scott M.; Nechyba, Michael C.; Ifju, Peter G.; Wazak, Martin

2002-01-01

Substantial progress has been made recently towards design building and test-flying remotely piloted Micro Air Vehicle's (MAVs). We seek to complement this progress in overcoming the aerodynamic obstacles to.flight at very small scales with a vision stability and autonomy system. The developed system based on a robust horizon detection algorithm which we discuss in greater detail in a companion paper. In this paper, we first motivate the use of computer vision for MAV autonomy arguing that given current sensor technology, vision may he the only practical approach to the problem. We then briefly review our statistical vision-based horizon detection algorithm, which has been demonstrated at 30Hz with over 99.9% correct horizon identification. Next we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feed-back controller for self-stabilized flight, and report results on vision autonomous flights of duration exceeding ten minutes.

Robustness of reduced-order observer-based controllers in transitional 2D Blasius boundary layers

NASA Astrophysics Data System (ADS)

Belson, Brandt; Semeraro, Onofrio; Rowley, Clarence; Pralits, Jan; Henningson, Dan

2011-11-01

In this work, we seek to delay transition in the Blasius boundary layer. We trip the flow with an upstream disturbance and dampen the growth of the resulting structures downstream. The observer-based controllers use a single sensor and a single localized body force near the wall. To formulate the controllers, we first find a reduced-order model of the system via the Eigensystem Realization Algorithm (ERA), then find the H2 optimal controller for this reduced-order system. We find the resulting controllers are effective only when the sensor is upstream of the actuator (in a feedforward configuration), but as is expected, are sensitive to model uncertainty. When the sensor is downstream of the actuator (in a feedback configuration), the reduced-order observer-based controllers are not robust and ineffective on the full system. In order to investigate the robustness properties of the system, an iterative technique called the adjoint of the direct adjoint (ADA) is employed to find a full-dimensional H2 optimal controller. This avoids the reduced-order modelling step and serves as a reference point. ADA is promising for investigating the lack of robustness previously mentioned.

Fully Dynamic Bin Packing

NASA Astrophysics Data System (ADS)

Ivković, Zoran; Lloyd, Errol L.

Classic bin packing seeks to pack a given set of items of possibly varying sizes into a minimum number of identical sized bins. A number of approximation algorithms have been proposed for this NP-hard problem for both the on-line and off-line cases. In this chapter we discuss fully dynamic bin packing, where items may arrive (Insert) and depart (Delete) dynamically. In accordance with standard practice for fully dynamic algorithms, it is assumed that the packing may be arbitrarily rearranged to accommodate arriving and departing items. The goal is to maintain an approximately optimal solution of provably high quality in a total amount of time comparable to that used by an off-line algorithm delivering a solution of the same quality.

Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

NASA Technical Reports Server (NTRS)

Balla, R. Jeffrey; Miller, Corey A.

2008-01-01

This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

Dynamic Imbalance Analysis and Stability Control of Galloping Gait for a Passive Quadruped Robot.

PubMed

Wang, Chunlei; Zhang, Ting; Wei, Xiaohui; Long, Yongjun; Wang, Shigang

2015-01-01

Some imbalance and balance postures of a passive quadruped robot with a simplified mathematical model are studied. Through analyzing the influence of the touchdown angle of the rear leg on the posture of the trunk during the flight phase, the stability criterion is concluded: the closer are the two moments which are the zero time of the pitching angle and the peak time of the center of mass, the better is the stability of the trunk posture during the flight phase. Additionally, the validity of the stability criterion is verified for the cat, greyhound, lion, racehorse, basset hound, and giraffe. Furthermore, the stability criterion is also applicable when the center of the mass of body is shifted. Based on the stability criterion, the necessary and sufficient condition of the galloping stability for the quadruped robot is proposed to attain a controlled thrust. The control strategy is designed by an optimization dichotomy algorithm for seeking the zero point of the balance condition. Through the control results, it is demonstrated that the imbalance posture of the trunk could be stabilized by adjusting the stiffness of four legs.

Distributed Nash Equilibrium Seeking for Generalized Convex Games with Shared Constraints

NASA Astrophysics Data System (ADS)

Sun, Chao; Hu, Guoqiang

2018-05-01

In this paper, we deal with the problem of finding a Nash equilibrium for a generalized convex game. Each player is associated with a convex cost function and multiple shared constraints. Supposing that each player can exchange information with its neighbors via a connected undirected graph, the objective of this paper is to design a Nash equilibrium seeking law such that each agent minimizes its objective function in a distributed way. Consensus and singular perturbation theories are used to prove the stability of the system. A numerical example is given to show the effectiveness of the proposed algorithms.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, Wei-Chen; Kendall, Donald R.; Putti, Mario; Yeh, William W.-G.

2009-08-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, W.; Kendall, D. R.; Putti, M.; Yeh, W. W.

2008-12-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measurement data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistently physical interpretation for pumping rate identification. The algorithm identifies unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rate, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study, we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

Challenges facing developers of CAD/CAM models that seek to predict human working postures

NASA Astrophysics Data System (ADS)

Wiker, Steven F.

2005-11-01

This paper outlines the need for development of human posture prediction models for Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) design applications in product, facility and work design. Challenges facing developers of posture prediction algorithms are presented and discussed.

Hello Worlds

ERIC Educational Resources Information Center

Kirschenbaum, Matthew

2009-01-01

The author advocates that humanities scholars should seek and study programming languages. He believes that, increasingly, an appreciation of how complex ideas can be imagined and expressed as a set of formal procedures--rules, models, algorithms--in the virtual space of a computer will be an essential element of a humanities education. Students…

Ocean Surface Wave Optical Roughness - Innovative Measurement and Modeling

DTIC Science & Technology

2010-01-01

Gemmrich et al., 2008) and microscale breaker crest length spectral density (e.g. Jessup and Phadnis , 2005) have been reported. Our effort seeks...and K.R. Phadnis , 2005: Measurement of the geometric and kinematic properties of microsacle breaking waves from infrared imagery using a PIV algorithm

Patients' intentions to seek medication information from pharmacists.

PubMed

Huston, Sally A

2013-01-01

To determine whether perceived medication use knowledge held and/or needed influenced intention to seek information from pharmacists, whether an information-intention relationship held after accounting for other variables, and whether asking medication use knowledge questions increased pharmacist information-seeking intention. Cross-sectional study. SETTING United States during July 2012. Qualtrics national panel members 21 years or older obtaining a new chronic medication within previous 30 days. Internet-administered survey. Medication information-seeking intention, medication knowledge held and needed, and pharmacist medication information-seeking intention. Although knowledge held and needed were initially significant, they became nonsignificant after adding affective and evaluative attitudes, perceived control, and risk. The final best-fitting model explained 21% of variance in pharmacist information-seeking intention. Patient intentions to seek information from pharmacists increased significantly after being asked medication use knowledge questions. Perceptions of medication risk, attitudes, and information-seeking control predict pharmacist information-seeking intention and offer pharmacists an opportunity to market information services.

First-aid algorithms in dental avulsion.

PubMed

Baginska, Joanna; Wilczynska-Borawska, Magdalena

2012-04-01

Almost one fourth of traumatic dental injuries occur at schools or in their surroundings. Prevalence of tooth avulsion varies from 0.5% to 16% of all cases of dental trauma. Children with dental avulsion may seek help from school nurses so they should be able to provide first-aid treatment. However, many studies showed that the general level of knowledge of medical staff concerning tooth avulsion is unsatisfactory and that it could be improved by educational activities. This article attempts to give short algorithms of first-aid management of avulsed tooth.

Preliminary supersonic flight test evaluation of performance seeking control

NASA Technical Reports Server (NTRS)

Orme, John S.; Gilyard, Glenn B.

1993-01-01

Digital flight and engine control, powerful onboard computers, and sophisticated controls techniques may improve aircraft performance by maximizing fuel efficiency, maximizing thrust, and extending engine life. An adaptive performance seeking control system for optimizing the quasi-steady state performance of an F-15 aircraft was developed and flight tested. This system has three optimization modes: minimum fuel, maximum thrust, and minimum fan turbine inlet temperature. Tests of the minimum fuel and fan turbine inlet temperature modes were performed at a constant thrust. Supersonic single-engine flight tests of the three modes were conducted using varied after burning power settings. At supersonic conditions, the performance seeking control law optimizes the integrated airframe, inlet, and engine. At subsonic conditions, only the engine is optimized. Supersonic flight tests showed improvements in thrust of 9 percent, increases in fuel savings of 8 percent, and reductions of up to 85 deg R in turbine temperatures for all three modes. The supersonic performance seeking control structure is described and preliminary results of supersonic performance seeking control tests are given. These findings have implications for improving performance of civilian and military aircraft.

On Establishing Big Data Wave Breakwaters with Analytics (Invited)

NASA Astrophysics Data System (ADS)

Riedel, M.

2013-12-01

The Research Data Alliance Big Data Analytics (RDA-BDA) Interest Group seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. RDA-BDA seeks to analyze different scientific domain applications and their potential use of various big data analytics techniques. A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. These combinations are complex since a wide variety of different data analysis algorithms exist (e.g. specific algorithms using GPUs of analyzing brain images) that need to work together with multiple analytical tools reaching from simple (iterative) map-reduce methods (e.g. with Apache Hadoop or Twister) to sophisticated higher level frameworks that leverage machine learning algorithms (e.g. Apache Mahout). These computational analysis techniques are often augmented with visual analytics techniques (e.g. computational steering on large-scale high performance computing platforms) to put the human judgement into the analysis loop or new approaches with databases that are designed to support new forms of unstructured or semi-structured data as opposed to the rather tradtional structural databases (e.g. relational databases). More recently, data analysis and underpinned analytics frameworks also have to consider energy footprints of underlying resources. To sum up, the aim of this talk is to provide pieces of information to understand big data analytics in the context of science and engineering using the aforementioned classification as the lighthouse and as the frame of reference for a systematic approach. This talk will provide insights about big data analytics methods in context of science within varios communities and offers different views of how approaches of correlation and causality offer complementary methods to advance in science and engineering today. The RDA Big Data Analytics Group seeks to understand what approaches are not only technically feasible, but also scientifically feasible. The lighthouse Goal of the RDA Big Data Analytics Group is a classification of clever combinations of various Technologies and scientific applications in order to provide clear recommendations to the scientific community what approaches are technicalla and scientifically feasible.

A decision support tool for vehicle infrastructure integration : understanding information effects and advancing data fusion algorithms for traffic management applications.

DOT National Transportation Integrated Search

2009-10-15

This research seeks to explore vehicle-to-vehicle information networks to understand the interplay : between the information communicated and traffic conditions on the network. A longer-term goal is to : develop a decision support tool for processing...

Aircraft Route Optimization using the A-Star Algorithm

DTIC Science & Technology

2014-03-27

Map Cost array allows a search for a route that not only seeks to minimize the distance travelled, but also considers other factors that may impact ...Rules (VFR) flight profile requires aviators to plan a 20-minute fuel reserve into the flight while an Instrument Flight Rules ( IFR ) flight profile

Division Quilts: A Measurement Model

ERIC Educational Resources Information Center

Pratt, Sarah S.; Lupton, Tina M.; Richardson, Kerri

2015-01-01

As teachers seek activities to assist students in understanding division as more than just the algorithm, they find many examples of division as fair sharing. However, teachers have few activities to engage students in a quotative (measurement) model of division. Efraim Fischbein and his colleagues (1985) defined two types of whole-number…

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

NASA Technical Reports Server (NTRS)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

A method for evaluating discoverability and navigability of recommendation algorithms.

PubMed

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis

2017-01-01

Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.

Internet-based interventions to promote mental health help-seeking in elite athletes: an exploratory randomized controlled trial.

PubMed

Gulliver, Amelia; Griffiths, Kathleen M; Christensen, Helen; Mackinnon, Andrew; Calear, Alison L; Parsons, Alison; Bennett, Kylie; Batterham, Philip J; Stanimirovic, Rosanna

2012-06-29

Mental disorders are more common in young adults than at any other life stage. Despite this, young people have low rates of seeking professional help for mental health problems. Young elite athletes have less positive attitudes toward seeking help than nonathletes and thus may be particularly unlikely to seek help. Interventions aimed at increasing help-seeking in young elite athletes are warranted. To test the feasibility and efficacy of three Internet-based interventions designed to increase mental health help-seeking attitudes, intentions, and behavior in young elite athletes compared with a control condition. We conducted a randomized controlled trial (RCT) of three brief fully automated Internet-based mental health help-seeking interventions with 59 young elite athletes recruited online in a closed trial in Australia. The interventions consisted of a mental health literacy and destigmatization condition, a feedback condition providing symptom levels, and a minimal content condition comprising a list of help-seeking resources, compared with a control condition (no intervention). We measured help-seeking attitudes, intentions and behavior using self-assessed surveys. Participation was open to elite athletes regardless of their mental health status or risk of mental illness. Of 120 athletes initially agreeing to participate, 59 (49%) submitted a preintervention or postintervention survey, or both, and were included in the present study. Adherence was satisfactory, with 48 (81%) participants visiting both weeks of assigned intervention material. None of the interventions yielded a significant increase in help-seeking attitudes, intentions, or behavior relative to control. However, at postintervention, there was a trend toward a greater increase in help-seeking behavior from formal sources for the mental health literacy/destigmatization condition compared with control (P = .06). This intervention was also associated with increased depression literacy (P = .003, P = .005) and anxiety literacy (P = .002, P = .001) relative to control at postintervention and 3-month follow-up, respectively, and a reduction in depression stigma relative to control at postintervention (P = .01, P = .12) and anxiety stigma at 3-month follow-up (P = .18, P = .02). The feedback and help-seeking list interventions did not improve depression or anxiety literacy or decrease stigmatizing attitudes to these conditions. However, the study findings should be treated with caution. Due to recruitment challenges, the achieved sample size fell significantly short of the target size and the study was underpowered. Accordingly, the results should be considered as providing preliminary pilot data only. This is the first RCT of an Internet-based mental health help-seeking intervention for young elite athletes. The results suggest that brief mental health literacy and destigmatization improves knowledge and may decrease stigma but does not increase help-seeking. However, since the trial was underpowered, a larger trial is warranted. 2009/373 (www.clinicaltrials.gov ID: NCT00940732), cited at http://www.webcitation.org/5ymsRLy9r.

A Comparative Analysis of the ADOS-G and ADOS-2 Algorithms: Preliminary Findings.

PubMed

Dorlack, Taylor P; Myers, Orrin B; Kodituwakku, Piyadasa W

2018-06-01

The Autism Diagnostic Observation Schedule (ADOS) is a widely utilized observational assessment tool for diagnosis of autism spectrum disorders. The original ADOS was succeeded by the ADOS-G with noted improvements. More recently, the ADOS-2 was introduced to further increase its diagnostic accuracy. Studies examining the validity of the ADOS have produced mixed findings, and pooled relationship trends between the algorithm versions are yet to be analyzed. The current review seeks to compare the relative merits of the ADOS-G and ADOS-2 algorithms, Modules 1-3. Eight studies met inclusion criteria for the review, and six were selected for paired comparisons of the sensitivity and specificity of the ADOS. Results indicate several contradictory findings, underscoring the importance of further study.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

Intelligent mobility research for robotic locomotion in complex terrain

NASA Astrophysics Data System (ADS)

Trentini, Michael; Beckman, Blake; Digney, Bruce; Vincent, Isabelle; Ricard, Benoit

2006-05-01

The objective of the Autonomous Intelligent Systems Section of Defence R&D Canada - Suffield is best described by its mission statement, which is "to augment soldiers and combat systems by developing and demonstrating practical, cost effective, autonomous intelligent systems capable of completing military missions in complex operating environments." The mobility requirement for ground-based mobile systems operating in urban settings must increase significantly if robotic technology is to augment human efforts in these roles and environments. The intelligence required for autonomous systems to operate in complex environments demands advances in many fields of robotics. This has resulted in large bodies of research in areas of perception, world representation, and navigation, but the problem of locomotion in complex terrain has largely been ignored. In order to achieve its objective, the Autonomous Intelligent Systems Section is pursuing research that explores the use of intelligent mobility algorithms designed to improve robot mobility. Intelligent mobility uses sensing, control, and learning algorithms to extract measured variables from the world, control vehicle dynamics, and learn by experience. These algorithms seek to exploit available world representations of the environment and the inherent dexterity of the robot to allow the vehicle to interact with its surroundings and produce locomotion in complex terrain. The primary focus of the paper is to present the intelligent mobility research within the framework of the research methodology, plan and direction defined at Defence R&D Canada - Suffield. It discusses the progress and future direction of intelligent mobility research and presents the research tools, topics, and plans to address this critical research gap. This research will create effective intelligence to improve the mobility of ground-based mobile systems operating in urban settings to assist the Canadian Forces in their future urban operations.

Can a brief biologically-based psychoeducational intervention reduce stigma and increase help-seeking intentions for depression in young people? A randomised controlled trial.

PubMed

Howard, Kerry A; Griffiths, Kathleen M; McKetin, Rebecca; Ma, Jennifer

2018-05-01

There is disagreement in the literature as to whether biological attribution increases or decreases stigma. This study investigated the effect of an online biological intervention on stigma and help-seeking intentions for depression among adolescents. A three-arm, pre-post test, double-blind randomised controlled trial (RCT) was used to compare the effects of a biological and a psychosocial intervention delivered online. Participants comprised secondary school students (N = 327) aged 16-19 years. Outcome measures included anticipated self-stigma for depression (primary), personal stigma, help-seeking intention for depression, and biological and psychosocial attribution. Neither the biological nor the psychosocial educational intervention significantly reduced anticipated self-stigma or personal stigma for depression relative to the control. However, a small increase in help-seeking intention for depression relative to the control was found for the biological educational condition. The study was undertaken over a single session and it is unknown whether the intervention effect on help-seeking intentions was sustained or would translate into help-seeking behaviour. A brief online biological education intervention did not alter stigma, but did promote a small increase in help-seeking intentions for depression among adolescents. This type of intervention may be a practical means for facilitating help-seeking among adolescents with current or future depression treatment needs.

Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Yadav, B.; Hatfield, K.

2017-12-01

We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Considering both the accuracy and the computational complexity of these algorithms, we identify the extremely randomized trees as the best performing algorithm for BFI prediction in ungauged basins.

Psychosocial Determinants of Cancer-Related Information Seeking among Cancer Patients

PubMed Central

SMITH-McLALLEN, AARON; FISHBEIN, MARTIN; HORNIK, ROBERT C.

2011-01-01

This study explores the utility of using the Integrative Model of Behavioral Prediction as a framework for predicting cancer patients’ intentions to seek information about their cancer from sources other than a physician, and to examine the relation between patient’s baseline intentions to seek information and their actual seeking behavior at follow-up. Within one year of their diagnosis with colon, breast, or prostate cancer, 1641 patients responded to a mailed questionnaire assessing intentions to seek cancer-related information from a source other than their doctor, as well as their attitudes, perceived normative pressure, and perceived behavioral control with respect to this behavior. In addition, the survey assessed their cancer-related information seeking. One year later, 1049 of these patients responded to a follow-up survey assessing cancer-related information seeking during the previous year. Attitudes, perceived normative pressure, and perceived behavioral control were predictive of information seeking intentions, though attitudes emerged as the primary predictor. Intentions to seek information, perceived normative pressure regarding information seeking, baseline information seeking behavior, and being diagnosed with stage 4 cancer were predictive of actual information seeking behavior at follow-up. Practical implications are discussed. PMID:21207310

Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks

NASA Astrophysics Data System (ADS)

Esfandiar, Pooya; Bonchi, Francesco; Gleich, David F.; Greif, Chen; Lakshmanan, Laks V. S.; On, Byung-Won

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and a quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.

Historical feature pattern extraction based network attack situation sensing algorithm.

PubMed

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

PubMed Central

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

Fast katz and commuters : efficient estimation of social relatedness in large networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

On, Byung-Won; Lakshmanan, Laks V. S.; Greif, Chen

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and amore » quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.« less

Diversified models for portfolio selection based on uncertain semivariance

NASA Astrophysics Data System (ADS)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

On the use of controls for subsonic transport performance improvement: Overview and future directions

NASA Technical Reports Server (NTRS)

Gilyard, Glenn; Espana, Martin

1994-01-01

Increasing competition among airline manufacturers and operators has highlighted the issue of aircraft efficiency. Fewer aircraft orders have led to an all-out efficiency improvement effort among the manufacturers to maintain if not increase their share of the shrinking number of aircraft sales. Aircraft efficiency is important in airline profitability and is key if fuel prices increase from their current low. In a continuing effort to improve aircraft efficiency and develop an optimal performance technology base, NASA Dryden Flight Research Center developed and flight tested an adaptive performance seeking control system to optimize the quasi-steady-state performance of the F-15 aircraft. The demonstrated technology is equally applicable to transport aircraft although with less improvement. NASA Dryden, in transitioning this technology to transport aircraft, is specifically exploring the feasibility of applying adaptive optimal control techniques to performance optimization of redundant control effectors. A simulation evaluation of a preliminary control law optimizes wing-aileron camber for minimum net aircraft drag. Two submodes are evaluated: one to minimize fuel and the other to maximize velocity. This paper covers the status of performance optimization of the current fleet of subsonic transports. Available integrated controls technologies are reviewed to define approaches using active controls. A candidate control law for adaptive performance optimization is presented along with examples of algorithm operation.

Coordinated control of wind generation and energy storage for power system frequency regulation

NASA Astrophysics Data System (ADS)

Baone, Chaitanya Ashok

Large-scale centralized synchronous generators have long been the primary actors in exercising active power and frequency control, and much of the existing grid control framework is predicated upon their dynamic terminal characteristics. Important among these characteristics is the inertia of such generators. These play key roles in determining the electromechanical stability of the electric power grid. Modern wind generator systems are partially or fully connected to the grid through power electronic interfaces, and hence do not present the same level of inertial coupling. The absence of inertial frequency response from modern wind generator systems is a topic of growing concern in power engineering practice, as the penetration of wind generation is expected to grow dramatically in the next few years. Solutions proposed in the literature have sought to address this problem by seeking to mimic the inherent inertial response characteristics of traditional synchronous generators via control loops added to wind generators. Recent literature has raised concerns regarding this approach, and the work here will further examine its shortcomings, motivating approaches that seek to optimally design for the characteristics of the equipment exercising the control, rather than forcing new technologies to mimic the characteristics of synchronous machines. In particular, this work will develop a new approach to power system frequency regulation, with features suited to distributed energy storage devices such as grid-scale batteries and wind turbine speed and blade pitch control. The dynamic characteristics of these new technologies are treated along with existing mechanisms, such as synchronous machine governor control, to develop a comprehensive multi-input control design approach. To make the method practically feasible for geographically distributed power systems, an observer-based distributed control design utilizing phasor measurement unit (PMU) signals along with local measurements is developed. In addition to the system-wide objective of frequency regulation, a local objective of reducing the wind turbine drivetrain stress is considered. Also, an algorithm is proposed to characterize the modal degrees of controllability and observability on a subspace of critical modes of the system, so that the most effective sensor and actuator locations to be used in the control design can be found.

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

Accelerating IMRT optimization by voxel sampling

NASA Astrophysics Data System (ADS)

Martin, Benjamin C.; Bortfeld, Thomas R.; Castañon, David A.

2007-12-01

This paper presents a new method for accelerating intensity-modulated radiation therapy (IMRT) optimization using voxel sampling. Rather than calculating the dose to the entire patient at each step in the optimization, the dose is only calculated for some randomly selected voxels. Those voxels are then used to calculate estimates of the objective and gradient which are used in a randomized version of a steepest descent algorithm. By selecting different voxels on each step, we are able to find an optimal solution to the full problem. We also present an algorithm to automatically choose the best sampling rate for each structure within the patient during the optimization. Seeking further improvements, we experimented with several other gradient-based optimization algorithms and found that the delta-bar-delta algorithm performs well despite the randomness. Overall, we were able to achieve approximately an order of magnitude speedup on our test case as compared to steepest descent.

An Airborne Conflict Resolution Approach Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Mondoloni, Stephane; Conway, Sheila

2001-01-01

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

Thirty Years After Marr's Vision: Levels of Analysis in Cognitive Science.

PubMed

Peebles, David; Cooper, Richard P

2015-04-01

Thirty years after the publication of Marr's seminal book Vision (Marr, 1982) the papers in this topic consider the contemporary status of his influential conception of three distinct levels of analysis for information-processing systems, and in particular the role of the algorithmic and representational level with its cognitive-level concepts. This level has (either implicitly or explicitly) been downplayed or eliminated both by reductionist neuroscience approaches from below that seek to account for behavior from the implementation level and by Bayesian approaches from above that seek to account for behavior in purely computational-level terms. Copyright © 2015 Cognitive Science Society, Inc.

Internalized model minority myth, Asian values, and help-seeking attitudes among Asian American students.

PubMed

Kim, Paul Youngbin; Lee, Donghun

2014-01-01

The present study examined cultural factors underlying help-seeking attitudes of Asian American college students (N = 106). Specifically, we explored internalized model minority myth as a predictor of help-seeking attitudes and tested an intrapersonal-interpersonal framework of Asian values as a mechanism by which the two are related. Results indicated that internalized model minority myth significantly predicted unfavorable help-seeking attitudes, and emotional self-control mediated this relationship. Interpersonal values and humility were nonsignificant mediators, contrary to our hypotheses. The findings suggest that the investigation of internalized model minority myth in help-seeking research is a worthwhile endeavor, and they also highlight emotional self-control as an important explanatory variable in help-seeking attitudes of Asian American college students.

Control algorithms and applications of the wavefront sensorless adaptive optics

NASA Astrophysics Data System (ADS)

Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen

2017-10-01

Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.

Combinatorial therapy discovery using mixed integer linear programming.

PubMed

Pang, Kaifang; Wan, Ying-Wooi; Choi, William T; Donehower, Lawrence A; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong

2014-05-15

Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. zhandong.liu@bcm.edu Supplementary data are available at Bioinformatics online.

Intelligent Systems for Stabilizing Mode-Locked Lasers and Frequency Combs: Machine Learning and Equation-Free Control Paradigms for Self-Tuning Optics

NASA Astrophysics Data System (ADS)

Kutz, J. Nathan; Brunton, Steven L.

2015-12-01

We demonstrate that a software architecture using innovations in machine learning and adaptive control provides an ideal integration platform for self-tuning optics. For mode-locked lasers, commercially available optical telecom components can be integrated with servocontrollers to enact a training and execution software module capable of self-tuning the laser cavity even in the presence of mechanical and/or environmental perturbations, thus potentially stabilizing a frequency comb. The algorithm training stage uses an exhaustive search of parameter space to discover best regions of performance for one or more objective functions of interest. The execution stage first uses a sparse sensing procedure to recognize the parameter space before quickly moving to the near optimal solution and maintaining it using the extremum seeking control protocol. The method is robust and equationfree, thus requiring no detailed or quantitatively accurate model of the physics. It can also be executed on a broad range of problems provided only that suitable objective functions can be found and experimentally measured.

High temperature, harsh environment sensors for advanced power generation systems

NASA Astrophysics Data System (ADS)

Ohodnicki, P. R.; Credle, S.; Buric, M.; Lewis, R.; Seachman, S.

2015-05-01

One mission of the Crosscutting Technology Research program at the National Energy Technology Laboratory is to develop a suite of sensors and controls technologies that will ultimately increase efficiencies of existing fossil-fuel fired power plants and enable a new generation of more efficient and lower emission power generation technologies. The program seeks to accomplish this mission through soliciting, managing, and monitoring a broad range of projects both internal and external to the laboratory which span sensor material and device development, energy harvesting and wireless telemetry methodologies, and advanced controls algorithms and approaches. A particular emphasis is placed upon harsh environment sensing for compatibility with high temperature, erosive, corrosive, and highly reducing or oxidizing environments associated with large-scale centralized power generation. An overview of the full sensors and controls portfolio is presented and a selected set of current and recent research successes and on-going projects are highlighted. A more detailed emphasis will be placed on an overview of the current research thrusts and successes of the in-house sensor material and device research efforts that have been established to support the program.

The safe environment for every kid model: impact on pediatric primary care professionals.

PubMed

Dubowitz, Howard; Lane, Wendy G; Semiatin, Joshua N; Magder, Laurence S; Venepally, Mamata; Jans, Merel

2011-04-01

To examine whether the Safe Environment for Every Kid (SEEK) model of enhanced primary care would improve the attitudes, knowledge, comfort, competence, and behavior of child health care professionals (HPs) regarding addressing major risk factors for child maltreatment (CM). In a cluster randomized controlled trial, 18 private practices were assigned to intervention (SEEK) or control groups. SEEK HPs received training on CM risk factors (eg, maternal depression). The SEEK model included the parent screening questionnaire and the participation of a social worker. SEEK's impact was evaluated in 3 ways: (1) the health professional questionnaire (HPQ), which assessed HPs' attitudes and practice regarding the targeted problems; (2) observations of HPs conducting checkups; and (3) review of children's medical records. The 102 HPs averaged 45 years of age; 68% were female, and 74% were in suburban practices. Comparing baseline scores with 6-, 18-, and 36-month follow-up data, the HPQ revealed significant (P < .05) improvement in the SEEK group compared with controls on addressing depression (6 months), substance abuse (18 months), intimate partner violence (6 and 18 months), and stress (6, 18, and 36 months), and in their comfort level and perceived competence (both at 6, 18, and 36 months). SEEK HPs screened for targeted problems more often than did controls based on observations 24 months after the initial training and the medical records (P < .001). The SEEK model led to significant and sustained improvement in several areas. This is a crucial first step in helping HPs address major psychosocial problems that confront many families. SEEK offers a modest yet promising enhancement of primary care.

Dynamics of the line-start reluctance motor with rotor made of SMC material

NASA Astrophysics Data System (ADS)

Smółka, Krzysztof; Gmyrek, Zbigniew

2017-12-01

Design and control of electric motors in such a way as to ensure the expected motor dynamics, are the problems studied for many years. Many researchers tried to solve this problem, for example by the design optimization or by the use of special control algorithms in electronic systems. In the case of low-power and fractional power motors, the manufacture cost of the final product is many times less than cost of electronic system powering them. The authors of this paper attempt to improve the dynamic of 120 W line-start synchronous reluctance motor, energized by 50 Hz mains (without any electronic systems). The authors seek a road enabling improvement of dynamics of the analyzed motor, by changing the shape and material of the rotor, in such a way to minimize the modification cost of the tools necessary for the motor production. After the initial selection, the analysis of four rotors having different tooth shapes, was conducted.

A Top-Down Approach to Designing the Computerized Adaptive Multistage Test

ERIC Educational Resources Information Center

Luo, Xiao; Kim, Doyoung

2018-01-01

The top-down approach to designing a multistage test is relatively understudied in the literature and underused in research and practice. This study introduced a route-based top-down design approach that directly sets design parameters at the test level and utilizes the advanced automated test assembly algorithm seeking global optimality. The…

Application of ant colony optimization to optimal foragaing theory: comparison of simulation and field results

USDA-ARS?s Scientific Manuscript database

Ant Colony Optimization (ACO) refers to the family of algorithms inspired by the behavior of real ants and used to solve combinatorial problems such as the Traveling Salesman Problem (TSP).Optimal Foraging Theory (OFT) is an evolutionary principle wherein foraging organisms or insect parasites seek ...

Syndromic treatment of gonococcal and chlamydial infections in women seeking primary care for the genital discharge syndrome: decision-making.

PubMed Central

Behets, F. M.; Miller, W. C.; Cohen, M. S.

2001-01-01

The syndromic treatment of gonococcal and chlamydial infections in women seeking primary care in clinics where resources are scarce, as recommended by WHO and implemented in many developing countries, necessitates a balance to be struck between overtreatment and undertreatment. The present paper identifies factors that are relevant to the selection of specific strategies for syndromic treatment in the above circumstances. Among them are the general aspects of decision-making and caveats concerning the rational decision-making approach. The positive and negative implications are outlined of providing or withholding treatment following a specific algorithm with a given accuracy to detect infection, i.e. sensitivity, specificity and predictive values. Other decision-making considerations that are identified are related to implementation and include the stability of risk factors with regard to time, space and the implementer, acceptability by stakeholders, and environmental constraints. There is a need to consider empirically developed treatment algorithms as a basis for policy discourse, to be evaluated together with the evidence, alternatives and arguments by the stakeholders. PMID:11731816

Lunar Module Environmental Control System Design Considerations and Failure Modes. Part 2

NASA Technical Reports Server (NTRS)

Interbartolo, Michael A.

2009-01-01

This viewgraph presentation seeks to describe the Lunar Module Environmental Control System (ECS) subsystem testing and redesign and seeks to summarize the in-flight failures of the Lunar Module (LM) Environmental Control System (ECS).

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2012-01-01

In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

Compressed sensing with gradient total variation for low-dose CBCT reconstruction

NASA Astrophysics Data System (ADS)

Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung

2015-06-01

This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

PubMed

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.

Improvement in the control aspect of laser frequency stabilization for SUNLITE project

NASA Technical Reports Server (NTRS)

Zia, Omar

1992-01-01

Flight Electronics Division of Langley Research Center is developing a spaceflight experiment called the Stanford University and NASA Laser In-Space Technology (SUNLITE). The objective of the project is to explore the fundamental limits on frequency stability using an FM laser locking technique on a Nd:YAG non-planar ring (free-running linewidth of 5 KHz) oscillator in the vibration free, microgravity environment of space. Compact and automated actively stabilized terahertz laser oscillators will operate in space with an expected linewidth of less than 3 Hz. To implement and verify this experiment, NASA engineers have designed and built a state of the art, space qualified high speed data acquisition system for measuring the linewidth and stability limits of a laser oscillator. In order to achieve greater stability and better performance, an active frequency control scheme requiring the use of a feedback control loop has been applied. In the summer of 1991, the application of control theory in active frequency control as a frequency stabilization technique was investigated. The results and findings were presented in 1992 at the American Control Conference in Chicago, and have been published in Conference Proceedings. The main focus was to seek further improvement in the overall performance of the system by replacing the analogue controller by a digital algorithm.

Dynamically Evolving Sectors for Convective Weather Impact

NASA Technical Reports Server (NTRS)

Drew, Michael C.

2010-01-01

A new strategy for altering existing sector boundaries in response to blocking convective weather is presented. This method seeks to improve the reduced capacity of sectors directly affected by weather by moving boundaries in a direction that offers the greatest capacity improvement. The boundary deformations are shared by neighboring sectors within the region in a manner that preserves their shapes and sizes as much as possible. This reduces the controller workload involved with learning new sector designs. The algorithm that produces the altered sectors is based on a force-deflection mesh model that needs only nominal traffic patterns and the shape of the blocking weather for input. It does not require weather-affected traffic patterns that would have to be predicted by simulation. When compared to an existing optimal sector design method, the sectors produced by the new algorithm are more similar to the original sector shapes, resulting in sectors that may be more suitable for operational use because the change is not as drastic. Also, preliminary results show that this method produces sectors that can equitably distribute the workload of rerouted weather-affected traffic throughout the region where inclement weather is present. This is demonstrated by sector aircraft count distributions of simulated traffic in weather-affected regions.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Goal orientation, self-regulation strategies, and job-seeking intensity in unemployed adults.

PubMed

Creed, Peter A; King, Vivien; Hood, Michelle; McKenzie, Robert

2009-05-01

At Time 1 (T1), the authors surveyed 277 unemployed adults using measures of human capital, goal orientation, self-regulation (emotion control, motivation control, work commitment), and job-seeking intensity. At Time 2 (T2), 4 months later, 155 participants indicated their reemployment outcomes in number of job interviews and number of job offers. Using T1 data, the authors tested the predictors of job-seeking intensity and whether self-regulation mediated between goal orientation and job-seeking intensity. Using T1 and T2 data, they tested for predictors of reemployment outcomes and whether job-seeking intensity mediated the relationship between T1 antecedent variables and the reemployment outcomes. Learning goal orientation and self-regulation predicted job-seeking intensity, and self-regulation mediated between learning goal orientation and job-seeking intensity. Job-seeking intensity did not mediate the relationship among human capital, goal orientation, and self-regulation variables and reemployment outcomes. (c) 2009 APA, all rights reserved.

Dynamic Imbalance Analysis and Stability Control of Galloping Gait for a Passive Quadruped Robot

PubMed Central

Wang, Chunlei; Zhang, Ting; Wei, Xiaohui; Long, Yongjun; Wang, Shigang

2015-01-01

Some imbalance and balance postures of a passive quadruped robot with a simplified mathematical model are studied. Through analyzing the influence of the touchdown angle of the rear leg on the posture of the trunk during the flight phase, the stability criterion is concluded: the closer are the two moments which are the zero time of the pitching angle and the peak time of the center of mass, the better is the stability of the trunk posture during the flight phase. Additionally, the validity of the stability criterion is verified for the cat, greyhound, lion, racehorse, basset hound, and giraffe. Furthermore, the stability criterion is also applicable when the center of the mass of body is shifted. Based on the stability criterion, the necessary and sufficient condition of the galloping stability for the quadruped robot is proposed to attain a controlled thrust. The control strategy is designed by an optimization dichotomy algorithm for seeking the zero point of the balance condition. Through the control results, it is demonstrated that the imbalance posture of the trunk could be stabilized by adjusting the stiffness of four legs. PMID:27110095

Control strategies for wind farm power optimization: LES study

NASA Astrophysics Data System (ADS)

Ciri, Umberto; Rotea, Mario; Leonardi, Stefano

2017-11-01

Turbines in wind farms operate in off-design conditions as wake interactions occur for particular wind directions. Advanced wind farm control strategies aim at coordinating and adjusting turbine operations to mitigate power losses in such conditions. Coordination is achieved by controlling on upstream turbines either the wake intensity, through the blade pitch angle or the generator torque, or the wake direction, through yaw misalignment. Downstream turbines can be adapted to work in waked conditions and limit power losses, using the blade pitch angle or the generator torque. As wind conditions in wind farm operations may change significantly, it is difficult to determine and parameterize the variations of the coordinated optimal settings. An alternative is model-free control and optimization of wind farms, which does not require any parameterization and can track the optimal settings as conditions vary. In this work, we employ a model-free optimization algorithm, extremum-seeking control, to find the optimal set-points of generator torque, blade pitch and yaw angle for a three-turbine configuration. Large-Eddy Simulations are used to provide a virtual environment to evaluate the performance of the control strategies under realistic, unsteady incoming wind. This work was supported by the National Science Foundation, Grants No. 1243482 (the WINDINSPIRE project) and IIP 1362033 (I/UCRC WindSTAR). TACC is acknowledged for providing computational time.

Embedded Reasoning Supporting Aerospace IVHM

DTIC Science & Technology

2007-01-01

c method (BIT or health assessment algorithm) which the monitoring diagnostic relies on input information tics and Astronautics In the diagram...viewing of the current health state of all monitored subsystems, while also providing a means to probe deeper in the event anomalous operation is...seeks to integrate detection , diagnostic, and prognostic capabilities with a hierarchical diagnostic reasoning architecture into a single

First-Aid Algorithms in Dental Avulsion

ERIC Educational Resources Information Center

Baginska, Joanna; Wilczynska-Borawska, Magdalena

2012-01-01

Almost one fourth of traumatic dental injuries occur at schools or in their surroundings. Prevalence of tooth avulsion varies from 0.5% to 16% of all cases of dental trauma. Children with dental avulsion may seek help from school nurses so they should be able to provide first-aid treatment. However, many studies showed that the general level of…

Algorithms for optimizing cross-overs in DNA shuffling.

PubMed

He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

2012-03-21

DNA shuffling generates combinatorial libraries of chimeric genes by stochastically recombining parent genes. The resulting libraries are subjected to large-scale genetic selection or screening to identify those chimeras with favorable properties (e.g., enhanced stability or enzymatic activity). While DNA shuffling has been applied quite successfully, it is limited by its homology-dependent, stochastic nature. Consequently, it is used only with parents of sufficient overall sequence identity, and provides no control over the resulting chimeric library. This paper presents efficient methods to extend the scope of DNA shuffling to handle significantly more diverse parents and to generate more predictable, optimized libraries. Our CODNS (cross-over optimization for DNA shuffling) approach employs polynomial-time dynamic programming algorithms to select codons for the parental amino acids, allowing for zero or a fixed number of conservative substitutions. We first present efficient algorithms to optimize the local sequence identity or the nearest-neighbor approximation of the change in free energy upon annealing, objectives that were previously optimized by computationally-expensive integer programming methods. We then present efficient algorithms for more powerful objectives that seek to localize and enhance the frequency of recombination by producing "runs" of common nucleotides either overall or according to the sequence diversity of the resulting chimeras. We demonstrate the effectiveness of CODNS in choosing codons and allocating substitutions to promote recombination between parents targeted in earlier studies: two GAR transformylases (41% amino acid sequence identity), two very distantly related DNA polymerases, Pol X and β (15%), and beta-lactamases of varying identity (26-47%). Our methods provide the protein engineer with a new approach to DNA shuffling that supports substantially more diverse parents, is more deterministic, and generates more predictable and more diverse chimeric libraries.

Design issues and caching strategies for CD-ROM-based multimedia storage

NASA Astrophysics Data System (ADS)

Shastri, Vijnan; Rajaraman, V.; Jamadagni, H. S.; Venkat-Rangan, P.; Sampath-Kumar, Srihari

1996-03-01

CD-ROMs have proliferated as a distribution media for desktop machines for a large variety of multimedia applications (targeted for a single-user environment) like encyclopedias, magazines and games. With CD-ROM capacities up to 3 GB being available in the near future, they will form an integral part of Video on Demand (VoD) servers to store full-length movies and multimedia. In the first section of this paper we look at issues related to the single- user desktop environment. Since these multimedia applications are highly interactive in nature, we take a pragmatic approach, and have made a detailed study of the multimedia application behavior in terms of the I/O request patterns generated to the CD-ROM subsystem by tracing these patterns. We discuss prefetch buffer design and seek time characteristics in the context of the analysis of these traces. We also propose an adaptive main-memory hosted cache that receives caching hints from the application to reduce the latency when the user moves from one node of the hyper graph to another. In the second section we look at the use of CD-ROM in a VoD server and discuss the problem of scheduling multiple request streams and buffer management in this scenario. We adapt the C-SCAN (Circular SCAN) algorithm to suit the CD-ROM drive characteristics and prove that it is optimal in terms of buffer size management. We provide computationally inexpensive relations by which this algorithm can be implemented. We then propose an admission control algorithm which admits new request streams without disrupting the continuity of playback of the previous request streams. The algorithm also supports operations such as fast forward and replay. Finally, we discuss the problem of optimal placement of MPEG streams on CD-ROMs in the third section.

Development of a Smart Release Algorithm for Mid-Air Separation of Parachute Test Articles

NASA Technical Reports Server (NTRS)

Moore, James W.

2011-01-01

The Crew Exploration Vehicle Parachute Assembly System (CPAS) project is currently developing an autonomous method to separate a capsule-shaped parachute test vehicle from an air-drop platform for use in the test program to develop and validate the parachute system for the Orion spacecraft. The CPAS project seeks to perform air-drop tests of an Orion-like boilerplate capsule. Delivery of the boilerplate capsule to the test condition has proven to be a critical and complicated task. In the current concept, the boilerplate vehicle is extracted from an aircraft on top of a Type V pallet and then separated from the pallet in mid-air. The attitude of the vehicles at separation is critical to avoiding re-contact and successfully deploying the boilerplate into a heatshield-down orientation. Neither the pallet nor the boilerplate has an active control system. However, the attitude of the mated vehicle as a function of time is somewhat predictable. CPAS engineers have designed an avionics system to monitor the attitude of the mated vehicle as it is extracted from the aircraft and command a release when the desired conditions are met. The algorithm includes contingency capabilities designed to release the test vehicle before undesirable orientations occur. The algorithm was verified with simulation and ground testing. The pre-flight development and testing is discussed and limitations of ground testing are noted. The CPAS project performed a series of three drop tests as a proof-of-concept of the release technique. These tests helped to refine the attitude instrumentation and software algorithm to be used on future tests. The drop tests are described in detail and the evolution of the release system with each test is described.

Coordinated Recruitment of Cortical-Subcortical Circuits and Ascending Dopamine and Serotonin Neurons During Inhibitory Control of Cocaine Seeking in Rats.

PubMed

Navailles, Sylvia; Guillem, Karine; Vouillac-Mendoza, Caroline; Ahmed, Serge H

2015-09-01

People with cocaine addiction retain some degree of prefrontal cortex (PFC) inhibitory control of cocaine craving, a brain capacity that may underlie the efficacy of cognitive behavioral therapy for addiction. Similar findings were recently found in rats after extended access to and escalation of cocaine self-administration. Rats' inhibitory control of cocaine seeking was flexible, sufficiently strong to suppress cocaine-primed reinstatement and depended, at least in part, on neuronal activity within the prelimbic (PL) PFC. Here, we used a large-scale and high-resolution Fos mapping approach to identify, beyond the PL PFC, how top-down and/or bottom-up PFC-subcortical circuits are recruited during inhibition of cocaine seeking. Overall, we found that effective inhibitory control of cocaine seeking is associated with the coordinated recruitment of different top-down cortical-striatal circuits originating from different PFC territories, and of different bottom-up dopamine (DA) and serotonin (5-HT) midbrain subsystems that normally modulate activity in these circuits. This integrated brain response suggests that rats concomitantly engage and experience intricate cognitive and affective processes when they have to inhibit intense cocaine seeking. Thus, even after extended drug use, rats can be successfully trained to engage whole-brain inhibitory control mechanisms to suppress cocaine seeking. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics.

PubMed

Dong, Yimeng; Gupta, Nirupam; Chopra, Nikhil

2016-11-01

In this paper, vulnerability of a distributed consensus seeking multi-agent system (MAS) with double-integrator dynamics against edge-bound content modification cyber attacks is studied. In particular, we define a specific edge-bound content modification cyber attack called malignant content modification attack (MCoMA), which results in unbounded growth of an appropriately defined group disagreement vector. Properties of MCoMA are utilized to design detection and mitigation algorithms so as to impart resilience in the considered MAS against MCoMA. Additionally, the proposed detection mechanism is extended to detect the general edge-bound content modification attacks (not just MCoMA). Finally, the efficacies of the proposed results are illustrated through numerical simulations.

Content modification attacks on consensus seeking multi-agent system with double-integrator dynamics

NASA Astrophysics Data System (ADS)

Dong, Yimeng; Gupta, Nirupam; Chopra, Nikhil

2016-11-01

In this paper, vulnerability of a distributed consensus seeking multi-agent system (MAS) with double-integrator dynamics against edge-bound content modification cyber attacks is studied. In particular, we define a specific edge-bound content modification cyber attack called malignant content modification attack (MCoMA), which results in unbounded growth of an appropriately defined group disagreement vector. Properties of MCoMA are utilized to design detection and mitigation algorithms so as to impart resilience in the considered MAS against MCoMA. Additionally, the proposed detection mechanism is extended to detect the general edge-bound content modification attacks (not just MCoMA). Finally, the efficacies of the proposed results are illustrated through numerical simulations.

A theory-based approach to understanding suicide risk in shelter-seeking women.

PubMed

Wolford-Clevenger, Caitlin; Smith, Phillip N

2015-04-01

Women seeking shelter from intimate partner violence are at an increased risk for suicide ideation and attempts compared to women in the general population. Control-based violence, which is common among shelter-seeking women, may play a pivotal role in the development of suicide ideation and attempts. Current risk assessment and management practices for shelter-seeking women are limited by the lack of an empirically grounded understanding of increased risk in this population. We argue that in order to more effectively promote risk assessment and management, an empirically supported theory that is sensitive to the experiences of shelter-seeking women is needed. Such a theory-driven approach has the benefits of identifying and prioritizing targetable areas for intervention. Here, we review the evidence for the link between coercive control and suicide ideation and attempts from the perspective of Baumeister's escape theory of suicide. This theory has the potential to explain the role of coercive control in the development of suicide ideation and eventual attempts in shelter-seeking women. Implications for suicide risk assessment and prevention in domestic violence shelters are discussed. © The Author(s) 2014.

Sustainable IT and IT for Sustainability

NASA Astrophysics Data System (ADS)

Liu, Zhenhua

Energy and sustainability have become one of the most critical issues of our generation. While the abundant potential of renewable energy such as solar and wind provides a real opportunity for sustainability, their intermittency and uncertainty present a daunting operating challenge. This thesis aims to develop analytical models, deployable algorithms, and real systems to enable efficient integration of renewable energy into complex distributed systems with limited information. The first thrust of the thesis is to make IT systems more sustainable by facilitating the integration of renewable energy into these systems. IT represents the fastest growing sectors in energy usage and greenhouse gas pollution. Over the last decade there are dramatic improvements in the energy efficiency of IT systems, but the efficiency improvements do not necessarily lead to reduction in energy consumption because more servers are demanded. Further, little effort has been put in making IT more sustainable, and most of the improvements are from improved "engineering" rather than improved "algorithms". In contrast, my work focuses on developing algorithms with rigorous theoretical analysis that improve the sustainability of IT. In particular, this thesis seeks to exploit the flexibilities of cloud workloads both (i) in time by scheduling delay-tolerant workloads and (ii) in space by routing requests to geographically diverse data centers. These opportunities allow data centers to adaptively respond to renewable availability, varying cooling efficiency, and fluctuating energy prices, while still meeting performance requirements. The design of the enabling algorithms is however very challenging because of limited information, non-smooth objective functions and the need for distributed control. Novel distributed algorithms are developed with theoretically provable guarantees to enable the "follow the renewables" routing. Moving from theory to practice, I helped HP design and implement industry's first Net-zero Energy Data Center. The second thrust of this thesis is to use IT systems to improve the sustainability and efficiency of our energy infrastructure through data center demand response. The main challenges as we integrate more renewable sources to the existing power grid come from the fluctuation and unpredictability of renewable generation. Although energy storage and reserves can potentially solve the issues, they are very costly. One promising alternative is to make the cloud data centers demand responsive. The potential of such an approach is huge. To realize this potential, we need adaptive and distributed control of cloud data centers and new electricity market designs for distributed electricity resources. My work is progressing in both directions. In particular, I have designed online algorithms with theoretically guaranteed performance for data center operators to deal with uncertainties under popular demand response programs. Based on local control rules of customers, I have further designed new pricing schemes for demand response to align the interests of customers, utility companies, and the society to improve social welfare.

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

The Myriad Strategies for Seeking Control in the Dying Process

ERIC Educational Resources Information Center

Schroepfer, Tracy A.; Noh, Hyunjin; Kavanaugh, Melinda

2009-01-01

Purpose: This study explored the role control plays in the dying process of terminally ill elders by investigating the aspects of the dying process over which they seek to exercise control, the strategies they use, and whether they desire to exercise more control. Design and Methods: In-depth face-to-face interviews were conducted with 84…

Reducing the stigma associated with seeking psychotherapy through self-affirmation.

PubMed

Lannin, Daniel G; Guyll, Max; Vogel, David L; Madon, Stephanie

2013-10-01

Psychotherapy may be underutilized because people experience self-stigma-the internalization of public stigma associated with seeking psychotherapy. The purpose of this study was to experimentally test whether the self-stigma associated with seeking psychotherapy could be reduced by a self-affirmation intervention wherein participants reflected on an important personal characteristic. Compared with a control group, we hypothesized that a self-affirmation writing task would attenuate self-stigma, and thereby evidence indirect effects on intentions and willingness to seek psychotherapy. Participants were 84 undergraduates experiencing psychological distress. After completing pretest measures of self-stigma, intentions, and willingness to seek psychotherapy, participants were randomly assigned to either a self-affirmation or a control writing task, and subsequently completed posttest measures of self-stigma, intentions, and willingness to seek psychotherapy. Consistent with hypotheses, participants who engaged in self-affirmation reported lower self-stigma at posttest. Moreover, the self-affirmation writing task resulted in a positive indirect effect on willingness to seek psychotherapy, though results failed to support an indirect effect on intentions to seek psychotherapy. Findings suggest that self-affirmation theory may provide a useful framework for designing interventions that seek to address the underutilization of psychological services through reductions in self-stigma.

Psychometric properties of the reassurance-seeking scale in a Turkish sample.

PubMed

Gençöz, Tülin; Gençöz, Faruk

2005-02-01

This study examined the psychometric properties of the Reassurance-Seeking Scale in a sample of 102 Turkish undergraduate students. High internal consistency reliability was found for the Reassurance-Seeking Scale (alpha=.86). Factor analysis of the scale identified a single component that accounted for 71% of the total variance. The scale was significantly positively correlated with the Beck Depression Inventory and Beck Anxiety Inventory and had a significantly negative correlation with the Rosenberg Self-esteem Scale. Partial correlations of Reassurance-seeking with Depression scores as controlled by Anxiety scores and with Anxiety scores as controlled by Depression scores indicated that Reassurance-seeking scores maintained association with Depression but not with Anxiety. All these findings were in line with expectations.

Analysis of breast thermograms for ROI extraction and description using mathematical morphology

NASA Astrophysics Data System (ADS)

Zermeño-Loreto, O. A.; Toxqui-Quitl, C.; Orozco Guillén, E. E.; Padilla-Vivanco, A.

2017-09-01

The detection of a temperature increase or hot spots in breast thermograms can be related with high metabolic activity of disease cells. Image processing algorithms to seek mainly temperature increases above 3°C which have a high probability of being a malignancy are proposed. Also a derivative operator is used to highlights breast regions of interest (ROI). In order to determinate a medical alert, a feature descriptor of the ROI is constructed using its maximum temperature, maximum increase of temperature, sector/quadrant position in the breast, and area. The proposed algorithms are tested in a home database and a public database for mastology research.

A new optimal seam method for seamless image stitching

NASA Astrophysics Data System (ADS)

Xue, Jiale; Chen, Shengyong; Cheng, Xu; Han, Ying; Zhao, Meng

2017-07-01

A novel optimal seam method which aims to stitch those images with overlapping area more seamlessly has been propos ed. Considering the traditional gradient domain optimal seam method and fusion algorithm result in bad color difference measurement and taking a long time respectively, the input images would be converted to HSV space and a new energy function is designed to seek optimal stitching path. To smooth the optimal stitching path, a simplified pixel correction and weighted average method are utilized individually. The proposed methods exhibit performance in eliminating the stitching seam compared with the traditional gradient optimal seam and high efficiency with multi-band blending algorithm.

A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments

NASA Technical Reports Server (NTRS)

McDowell, Mark

2008-01-01

An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

PubMed

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2012-05-01

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated Spectroscopic Analysis Using the Particle Swarm Optimization Algorithm: Implementing a Guided Search Algorithm to Autofit

NASA Astrophysics Data System (ADS)

Ervin, Katherine; Shipman, Steven

2017-06-01

While rotational spectra can be rapidly collected, their analysis (especially for complex systems) is seldom straightforward, leading to a bottleneck. The AUTOFIT program was designed to serve that need by quickly matching rotational constants to spectra with little user input and supervision. This program can potentially be improved by incorporating an optimization algorithm in the search for a solution. The Particle Swarm Optimization Algorithm (PSO) was chosen for implementation. PSO is part of a family of optimization algorithms called heuristic algorithms, which seek approximate best answers. This is ideal for rotational spectra, where an exact match will not be found without incorporating distortion constants, etc., which would otherwise greatly increase the size of the search space. PSO was tested for robustness against five standard fitness functions and then applied to a custom fitness function created for rotational spectra. This talk will explain the Particle Swarm Optimization algorithm and how it works, describe how Autofit was modified to use PSO, discuss the fitness function developed to work with spectroscopic data, and show our current results. Seifert, N.A., Finneran, I.A., Perez, C., Zaleski, D.P., Neill, J.L., Steber, A.L., Suenram, R.D., Lesarri, A., Shipman, S.T., Pate, B.H., J. Mol. Spec. 312, 13-21 (2015)

Exact Algorithms for Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

PubMed

Kordi, Misagh; Bansal, Mukul S

2017-06-01

Duplication-Transfer-Loss (DTL) reconciliation is a powerful method for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation seeks to reconcile gene trees with species trees by postulating speciation, duplication, transfer, and loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. In practice, however, gene trees are often non-binary due to uncertainty in the gene tree topologies, and DTL reconciliation with non-binary gene trees is known to be NP-hard. In this paper, we present the first exact algorithms for DTL reconciliation with non-binary gene trees. Specifically, we (i) show that the DTL reconciliation problem for non-binary gene trees is fixed-parameter tractable in the maximum degree of the gene tree, (ii) present an exponential-time, but in-practice efficient, algorithm to track and enumerate all optimal binary resolutions of a non-binary input gene tree, and (iii) apply our algorithms to a large empirical data set of over 4700 gene trees from 100 species to study the impact of gene tree uncertainty on DTL-reconciliation and to demonstrate the applicability and utility of our algorithms. The new techniques and algorithms introduced in this paper will help biologists avoid incorrect evolutionary inferences caused by gene tree uncertainty.

Positive thinking elevates tolerance: Experimental effects of happiness on adolescents' attitudes toward asylum seekers.

PubMed

Tenenbaum, Harriet R; Capelos, Tereza; Lorimer, Jessica; Stocks, Thomas

2018-04-01

Inducing emotional reactions toward social groups can influence individuals' political tolerance. This study examines the influence of incidental fear and happiness on adolescents' tolerant attitudes and feelings toward young Muslim asylum seekers. In our experiment, 219 16- to 21-year-olds completed measures of prejudicial attitudes. After being induced to feel happiness, fear, or no emotion (control), participants reported their tolerant attitudes and feelings toward asylum-seeking young people. Participants assigned to the happiness condition demonstrated more tolerant attitudes toward asylum-seeking young people than did those assigned to the fear or control conditions. Participants in the control condition did not differ from participants in the fear condition. The participants in the happiness condition also had more positive feelings toward asylum-seeking young people than did participants in the control condition. The findings suggest that one way to increase positive attitudes toward asylum-seeking young people is to improve general emotional state.

[Wavelet packet extraction and entropy analysis of telemetry EEG from the prelimbic cortex of medial prefrontal cortex in morphine-induced CPP rats].

PubMed

Bai, Yu; Bai, Jia-Ming; Li, Jing; Li, Min; Yu, Ran; Pan, Qun-Wan

2014-12-25

The purpose of the present study is to analyze the relationship between the telemetry electroencephalogram (EEG) changes of the prelimbic (PL) cortex and the drug-seeking behavior of morphine-induced conditioned place preference (CPP) rats by using the wavelet packet extraction and entropy measurement. The recording electrode was stereotactically implanted into the PL cortex of rats. The animals were then divided randomly into operation-only control and morphine-induced CPP groups, respectively. A CPP video system in combination with an EEG wireless telemetry device was used for recording EEG of PL cortex when the rats shuttled between black-white or white-black chambers. The telemetry recorded EEGs were analyzed by wavelet packet extraction, Welch power spectrum estimate, normalized amplitude and Shannon entropy algorithm. The results showed that, compared with operation-only control group, the left PL cortex's EEG of morphine-induced CPP group during black-white chamber shuttling exhibited the following changes: (1) the amplitude of average EEG for each frequency bands extracted by wavelet packet was reduced; (2) the Welch power intensity was increased significantly in 10-50 Hz EEG band (P < 0.01 or P < 0.05); (3) Shannon entropy was increased in β, γ₁, and γ₂waves of the EEG (P < 0.01 or P < 0.05); and (4) the average information entropy was reduced (P < 0.01). The results suggest that above mentioned EEG changes in morphine-induced CPP group rat may be related to animals' drug-seeking motivation and behavior launching.

Parallel implementation of D-Phylo algorithm for maximum likelihood clusters.

PubMed

Malik, Shamita; Sharma, Dolly; Khatri, Sunil Kumar

2017-03-01

This study explains a newly developed parallel algorithm for phylogenetic analysis of DNA sequences. The newly designed D-Phylo is a more advanced algorithm for phylogenetic analysis using maximum likelihood approach. The D-Phylo while misusing the seeking capacity of k -means keeps away from its real constraint of getting stuck at privately conserved motifs. The authors have tested the behaviour of D-Phylo on Amazon Linux Amazon Machine Image(Hardware Virtual Machine)i2.4xlarge, six central processing unit, 122 GiB memory, 8  ×  800 Solid-state drive Elastic Block Store volume, high network performance up to 15 processors for several real-life datasets. Distributing the clusters evenly on all the processors provides us the capacity to accomplish a near direct speed if there should arise an occurrence of huge number of processors.

A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

PubMed

Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

2017-02-01

Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

SymDex: increasing the efficiency of chemical fingerprint similarity searches for comparing large chemical libraries by using query set indexing.

PubMed

Tai, David; Fang, Jianwen

2012-08-27

The large sizes of today's chemical databases require efficient algorithms to perform similarity searches. It can be very time consuming to compare two large chemical databases. This paper seeks to build upon existing research efforts by describing a novel strategy for accelerating existing search algorithms for comparing large chemical collections. The quest for efficiency has focused on developing better indexing algorithms by creating heuristics for searching individual chemical against a chemical library by detecting and eliminating needless similarity calculations. For comparing two chemical collections, these algorithms simply execute searches for each chemical in the query set sequentially. The strategy presented in this paper achieves a speedup upon these algorithms by indexing the set of all query chemicals so redundant calculations that arise in the case of sequential searches are eliminated. We implement this novel algorithm by developing a similarity search program called Symmetric inDexing or SymDex. SymDex shows over a 232% maximum speedup compared to the state-of-the-art single query search algorithm over real data for various fingerprint lengths. Considerable speedup is even seen for batch searches where query set sizes are relatively small compared to typical database sizes. To the best of our knowledge, SymDex is the first search algorithm designed specifically for comparing chemical libraries. It can be adapted to most, if not all, existing indexing algorithms and shows potential for accelerating future similarity search algorithms for comparing chemical databases.

Birkhoffian symplectic algorithms derived from Hamiltonian symplectic algorithms

NASA Astrophysics Data System (ADS)

Xin-Lei, Kong; Hui-Bin, Wu; Feng-Xiang, Mei

2016-01-01

In this paper, we focus on the construction of structure preserving algorithms for Birkhoffian systems, based on existing symplectic schemes for the Hamiltonian equations. The key of the method is to seek an invertible transformation which drives the Birkhoffian equations reduce to the Hamiltonian equations. When there exists such a transformation, applying the corresponding inverse map to symplectic discretization of the Hamiltonian equations, then resulting difference schemes are verified to be Birkhoffian symplectic for the original Birkhoffian equations. To illustrate the operation process of the method, we construct several desirable algorithms for the linear damped oscillator and the single pendulum with linear dissipation respectively. All of them exhibit excellent numerical behavior, especially in preserving conserved quantities. Project supported by the National Natural Science Foundation of China (Grant No. 11272050), the Excellent Young Teachers Program of North China University of Technology (Grant No. XN132), and the Construction Plan for Innovative Research Team of North China University of Technology (Grant No. XN129).

Bounded extremum seeking for angular velocity actuated control of nonholonomic unicycle

DOE PAGES

Scheinker, Alexander

2016-08-17

Here, we study control of the angular-velocity actuated nonholonomic unicycle, via a simple, bounded extremum seeking controller which is robust to external disturbances and measurement noise. The vehicle performs source seeking despite not having any position information about itself or the source, able only to sense a noise corrupted scalar value whose extremum coincides with the unknown source location. In order to control the angular velocity, rather than the angular heading directly, a controller is developed such that the closed loop system exhibits multiple time scales and requires an analysis approach expanding the previous work of Kurzweil, Jarnik, Sussmann, andmore » Liu, utilizing weak limits. We provide analytic proof of stability and demonstrate how this simple scheme can be extended to include position-independent source seeking, tracking, and collision avoidance of groups on autonomous vehicles in GPS-denied environments, based only on a measure of distance to an obstacle, which is an especially important feature for an autonomous agent.« less

Algorithmic formulation of control problems in manipulation

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1975-01-01

The basic characteristics of manipulator control algorithms are discussed. The state of the art in the development of manipulator control algorithms is briefly reviewed. Different end-point control techniques are described together with control algorithms which operate on external sensor (imaging, proximity, tactile, and torque/force) signals in realtime. Manipulator control development at JPL is briefly described and illustrated with several figures. The JPL work pays special attention to the front or operator input end of the control algorithms.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE PAGES

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik; ...

2017-07-25

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molzahn, Daniel K.; Dorfler, Florian K.; Sandberg, Henrik

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. Here, this paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Repeated restraint stress exposure during early withdrawal accelerates incubation of cue-induced cocaine craving.

PubMed

Glynn, Ryan M; Rosenkranz, J Amiel; Wolf, Marina E; Caccamise, Aaron; Shroff, Freya; Smith, Alyssa B; Loweth, Jessica A

2018-01-01

A major challenge for treating cocaine addiction is the propensity for abstinent users to relapse. Two important triggers for relapse are cues associated with prior drug use and stressful life events. To study their interaction in promoting relapse during abstinence, we used the incubation model of craving and relapse in which cue-induced drug seeking progressively intensifies ('incubates') during withdrawal from extended-access cocaine self-administration. We tested rats for cue-induced cocaine seeking on withdrawal day (WD) 1. Rats were then subjected to repeated restraint stress or control conditions (seven sessions held between WD6 and WD14). All rats were tested again for cue-induced cocaine seeking on WD15, 1 day after the last stress or control session. Although controls showed a time-dependent increase in cue-induced cocaine seeking (incubation), rats exposed to repeated stress in early withdrawal exhibited a more robust increase in seeking behavior between WD1 and WD15. In separate stressed and control rats, equivalent cocaine seeking was observed on WD48. These results indicate that repeated stress in early withdrawal accelerates incubation of cocaine craving, although craving plateaus at the same level were observed in controls. However, 1 month after the WD48 test, rats subjected to repeated stress in early withdrawal showed enhanced cue-induced cocaine seeking following acute (24 hours) food deprivation stress. Together, these data indicate that chronic stress exposure enhances the initial rate of incubation of craving during early withdrawal, resulting in increased vulnerability to cue-induced relapse during this period, and may lead to a persistent increase in vulnerability to the relapse-promoting effects of stress. © 2016 Society for the Study of Addiction.

Study of Barrier to Help Seeking and its Relationships with Disability in Patients with Headache.

PubMed

John, Deepa; Ram, Dushad; Sundarmurthy, Harsha; Rathod, Harshal; Rathod, Snehal

2016-10-01

Headache is among the first three most prevalent disorders with a wide treatment gap due to barriers in help seeking. Headache has been associated with disability. However, the relationship of barriers to help-seeking and disability are unexplored. To find out the barriers to help seeking and its relationship with headache related disability in patients with headache. In this hospital based cross-sectional study, 200 consecutive subjects with headache attending a tertiary care centre were recruited as per selection criteria and assessed with Sociodemographic & Clinical Proforma, Mini International Neuropsychiatric Interview (MINI), Barriers to Help Seeking Scale (BHSS), The Henry Ford Hospital Headache Disability Inventory (HDI). High mean score was observed on BHSS subscale need for control and self reliance (19.45; SD ±9.66) and minimizing problem and resignation (10.02; SD ±6.98). Mean score on the HDI was 25.65 (SD ± 14.09). Socioeconomic status of the patient was statistically significant and positively associated with need for control and self reliance (p=0.035), concrete barriers and distrust of care givers (p=0.039), emotional control (p=0.005), and privacy (p=0.002). Occupational status had significant association with need for control and self-reliance (p=0.01), minimizing problem and resignation (p=0.033), and emotional control (p=0.006). Score on hospital headache disability inventory significantly predicted the value of score on concrete barriers and distrust of caregivers domain of HDI (p=0.001). Autonomy and under estimation of seriousness of headache are common barriers to help seeking. Pattern of help seeking barriers may vary with socio-economic status and occupational status, while disability varies with gender and severity of headache. Headache associated disability is positively associated with concrete barriers.

Risk Propensity, Risk Perception, and Sensation Seeking in US Army Soldiers: A Preliminary Study of a Risk Assessment Task Battery

DTIC Science & Technology

2010-10-01

Specifically, if deployment is retrospectively perceived as very risky or dangerous , then the perceived risk of negative consequences of risky behaviors...preference for various types of risky activities. There are five sub-scales of the EVAR; self-control, danger seeking, energy, impulsiveness, and...the total and five sub-scale (self-control, danger seeking, energy, impulsiveness, and invincibility) scores were calculated. A repeated measures

14 CFR 61.94 - Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations...

Code of Federal Regulations, 2012 CFR

2012-01-01

... 14 Aeronautics and Space 2 2012-01-01 2012-01-01 false Student pilot seeking a sport pilot... Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations at... operational control tower in other airspace. (a) A student pilot seeking a sport pilot certificate or a...

14 CFR 61.94 - Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations...

Code of Federal Regulations, 2013 CFR

2013-01-01

... 14 Aeronautics and Space 2 2013-01-01 2013-01-01 false Student pilot seeking a sport pilot... Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations at... operational control tower in other airspace. (a) A student pilot seeking a sport pilot certificate or a...

14 CFR 61.94 - Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations...

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 2 2014-01-01 2014-01-01 false Student pilot seeking a sport pilot... Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations at... operational control tower in other airspace. (a) A student pilot seeking a sport pilot certificate or a...

14 CFR 61.94 - Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations...

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 2 2011-01-01 2011-01-01 false Student pilot seeking a sport pilot... Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations at... operational control tower in other airspace. (a) A student pilot seeking a sport pilot certificate or a...

14 CFR 61.94 - Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations...

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 2 2010-01-01 2010-01-01 false Student pilot seeking a sport pilot... Student pilot seeking a sport pilot certificate or a recreational pilot certificate: Operations at... operational control tower in other airspace. (a) A student pilot seeking a sport pilot certificate or a...

Examining the Impact of Public Service Announcements on Help Seeking and Stigma: Results of a Randomized Controlled Trial.

PubMed

Corrigan, Patrick W; Powell, Karina J; Al-Khouja, Maya A

2015-11-01

Health communication campaigns often seek to diminish stigma and promote care seeking, with public service announcements (PSAs) frequently prominent in these campaigns. One example is the Australian-based beyondblue campaign. As an alternative approach, campaigns may seek to reduce stigma by promoting stories of recovery. Participants completed measures of stigmatizing and empowering attitudes at pre-, post-, and 72-hour follow-up after being randomized to a PSA recovery-oriented video, treatable disease-oriented video (beyondblue), or control. When exposed to the recovery-oriented PSA, participants showed significant reduction in stigmatizing attitudes from pre- to posttest than beyondblue or the control group with the emergence of nonsignificant trends identified at follow-up. Findings suggest a recovery-oriented video leads to better change on measures of stigma and affirming attitudes than beyondblue. Despite the aforementioned findings, results failed to show either the recovery or beyondblue videos had a significant impact on intent to seek treatment.

Silence is deadly: a cluster-randomised controlled trial of a mental health help-seeking intervention for young men.

PubMed

Calear, Alison L; Banfield, Michelle; Batterham, Philip J; Morse, Alyssa R; Forbes, Owen; Carron-Arthur, Bradley; Fisk, Martin

2017-10-23

Young men are consistently less likely to seek help for mental health problems than their female peers. This is particularly concerning given the high rates of suicide among male adolescents. The school system has been identified as an ideal setting for the implementation of prevention and early intervention programs for young people. The current trial aims to determine the effectiveness of the Silence is Deadly program in increasing positive help-seeking intentions for mental health problems and suicide among male secondary school students. This study is a two-arm, cluster-randomised, controlled trial that will compare the Silence is Deadly program to a wait-list control condition. Eight Australian high schools will be recruited to the trial, with male students in grades 11 and 12 (16 to 18 years of age) targeted for participation. The program is an innovative male-tailored suicide prevention intervention, comprising a presentation that emphasises role-modelling and legitimises help-seeking for personal and emotional problems, and a brief video that features celebrity athletes who counter existing male norms around help-seeking and encourage communication about personal and emotional issues. The program also includes a discussion of how to help a friend in distress and ends with a question and answer session. The primary outcome measure for the current study is help-seeking intentions. Secondary outcomes include help-seeking behaviour, help-seeking attitudes, help-seeking stigma, mental health symptoms, and suicidal ideation. Data will be collected pre-intervention, post-intervention, and at 3-month follow-up. Primary analyses will compare changes in help-seeking intentions for the intervention condition relative to the wait-list control condition using mixed-effects repeated-measures analyses that account for clustering within schools. If proven to be effective, this targeted help-seeking intervention for adolescent males, which is currently only delivered in one jurisdiction, could be more widely delivered in Australian high schools. The Silence is Deadly program has the potential to significantly contribute to the mental health of young men in Australia by improving help-seeking for suicidality and mental health problems, allowing this population to better access treatment and support sooner. Australian New Zealand Clinical Trials Registry, ACTRN12617000658314 . Registered on 8 May 2017.

Controlling Behaviors in Middle School Youth's Dating Relationships: Reactions and Help-Seeking Behaviors

ERIC Educational Resources Information Center

Elias-Lambert, Nada; Black, Beverly M.; Chigbu, Kingsley U.

2014-01-01

This exploratory study examined middle school students' (N = 380) help-seeking behaviors and other reactions to controlling behaviors in their dating relationships. Over three-fourths of the participants perpetrated and were victimized by controlling behaviors in their dating relationships. Youth used emotional/verbal and dominance/isolation forms…

A Robustly Stabilizing Model Predictive Control Algorithm

NASA Technical Reports Server (NTRS)

Ackmece, A. Behcet; Carson, John M., III

2007-01-01

A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

Understanding conflict-resolution taskload: Implementing advisory conflict-detection and resolution algorithms in an airspace

NASA Astrophysics Data System (ADS)

Vela, Adan Ernesto

2011-12-01

From 2010 to 2030, the number of instrument flight rules aircraft operations handled by Federal Aviation Administration en route traffic centers is predicted to increase from approximately 39 million flights to 64 million flights. The projected growth in air transportation demand is likely to result in traffic levels that exceed the abilities of the unaided air traffic controller in managing, separating, and providing services to aircraft. Consequently, the Federal Aviation Administration, and other air navigation service providers around the world, are making several efforts to improve the capacity and throughput of existing airspaces. Ultimately, the stated goal of the Federal Aviation Administration is to triple the available capacity of the National Airspace System by 2025. In an effort to satisfy air traffic demand through the increase of airspace capacity, air navigation service providers are considering the inclusion of advisory conflict-detection and resolution systems. In a human-in-the-loop framework, advisory conflict-detection and resolution decision-support tools identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft. A number of researchers and air navigation service providers hypothesize that the inclusion of combined conflict-detection and resolution tools into air traffic control systems will reduce or transform controller workload and enable the required increases in airspace capacity. In an effort to understand the potential workload implications of introducing advisory conflict-detection and resolution tools, this thesis provides a detailed study of the conflict event process and the implementation of conflict-detection and resolution algorithms. Specifically, the research presented here examines a metric of controller taskload: how many resolution commands an air traffic controller issues under the guidance of a conflict-detection and resolution decision-support tool. The goal of the research is to understand how the formulation, capabilities, and implementation of conflict-detection and resolution tools affect the controller taskload (system demands) associated with the conflict-resolution process, and implicitly the controller workload (physical and psychological demands). Furthermore this thesis seeks to establish best practices for the design of future conflict-detection and resolution systems. To generalize conclusions on the conflict-resolution taskload and best design practices of conflict-detection and resolution systems, this thesis focuses on abstracting and parameterizing the behaviors and capabilities of the advisory tools. Ideally, this abstraction of advisory decision-support tools serves as an alternative to exhaustively designing tools, implementing them in high-fidelity simulations, and analyzing their conflict-resolution taskload. Such an approach of simulating specific conflict-detection and resolution systems limits the type of conclusions that can be drawn concerning the design of more generic algorithms. In the process of understanding conflict-detection and resolution systems, evidence in the thesis reveals that the most effective approach to reducing conflict-resolution taskload is to improve conflict-detection systems. Furthermore, studies in the this thesis indicate that there is significant exibility in the design of conflict-resolution algorithms.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

PubMed

Zhang, He-Hua; Yang, Liuyang; Liu, Yuchuan; Wang, Pin; Yin, Jun; Li, Yongming; Qiu, Mingguo; Zhu, Xueru; Yan, Fang

2016-11-16

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

The health burden of breast hypertrophy.

PubMed

Kerrigan, C L; Collins, E D; Striplin, D; Kim, H M; Wilkins, E; Cunningham, B; Lowery, J

2001-11-01

Women seeking consultation for the surgical relief of symptoms associated with breast hypertrophy have been the focus of many studies. In contrast, little is known about those women with breast hypertrophy who do not seek symptomatic relief. The purpose of this study was to describe the health burden of breast hypertrophy by using a set of validated questionnaires and to compare women with breast hypertrophy who seek surgical treatment with those who do not. In addition, this latter group was compared with a group of control women without breast hypertrophy. Women seeking consultation for surgery were recruited from 14 plastic-surgery practices. Control subjects were recruited by advertisements in primary-care offices and newspapers. Women were asked to complete a self-report questionnaire that included the European Quality of Life (EuroQol) questionnaire, McGill Pain Questionnaire, Multidimensional Body Self Relations Questionnaire (MBSRQ), the Short Form-36 (SF-36) questionnaire, and questions regarding breast-related symptoms, comorbidities, and bra size. Descriptive statistics were compiled for three groups of women: (1) hypertrophy patients seeking surgical care, (2) hypertrophy control subjects (those whose reported bra-cup size was a D or larger), and (3) normal control subjects (those whose reported bra-cup size was an A, B, or C). The multiple linear regression method was used to compare the health burdens across groups while adjusting for other variables. Two hundred ninety-one women seeking surgical care and 195 control subjects were enrolled in the study. The 184 control subjects with bra-cup information available were further separated into 88 hypertrophy control subjects and 96 normal control subjects. In the control group, bra-cup size was correlated with health-burden measures, whereas in the surgical candidates, it was not. When scores were compared across the three groups, significant differences were found in all health-burden measures. The surgical candidates scored more poorly on the EuroQol utility, McGill pain rating index, MBSRQ appearance evaluation, physical component scale of the SF-36, and on breast symptoms than did the two control groups. In addition, the hypertrophy control subjects scored more poorly than the normal control subjects. With multiple linear regression analysis incorporating important potential confounders, the poorer scores in the surgical candidates remained statistically significant. It was concluded that breast hypertrophy in those seeking surgical care and those not seeking surgery has a significant impact on women's quality of life as measured by validated and widely used self-report instruments including the EuroQol, MBSRQ, McGill Pain Questionnaire, and the SF-36. Likewise, a new assessment instrument for breast-related symptoms also demonstrated greater symptomatology in women with breast hypertrophy.

Factors driving public tolerance levels and information‐seeking behaviour concerning insects in the household environment

PubMed Central

Poortvliet, P Marijn; Takken, Willem

2018-01-01

Abstract BACKGROUND The public's negative attitudes towards household insects drive tolerance for these insects and their control. Tolerance levels are important in integrated pest management (IPM), as are pest knowledge and information. The risk information seeking and processing (RISP) model describes the relationships between personal factors and information‐seeking behaviour. We combined IPM and RISP to determine important relationships between factors driving insect tolerance levels and information‐seeking behaviour through an online survey and tested whether this model is valid and generally applicable. RESULTS Relationships between variables from both IPM and RISP models were tested for seven insect species. Tolerance levels were measured with two factors: willingness to pay for pest control and whether insects are tolerated. Willingness to pay for control was positively affected by age, experience, risk perception, insect characteristics, and negative emotions and affected behavioural intention, by influencing information sufficiency and information‐seeking behaviour. Tolerability was influenced by perception of insect characteristics and determines whether control measures are taken. CONCLUSION It was possible to combine the RISP and IPM models. Relevant driving factors were a person's age, experience, risk perception, negative affective responses, tolerance levels, relevant channel beliefs about online forums, information sufficiency and information‐seeking behaviour. There was, however, variation in important factors between different insects. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. PMID:29274106

Systems and Methods for Peak-Seeking Control

NASA Technical Reports Server (NTRS)

Ryan, John J (Inventor); Speyer, Jason L (Inventor)

2013-01-01

A computerized system and method for peak-seeking-control that uses a unique Kalman filter design to optimize a control loop, in real time, to either maximize or minimize a performance function of a physical object ("plant"). The system and method achieves more accurate and efficient peak-seeking-control by using a time-varying Kalman filter to estimate both the performance function gradient (slope) and Hessian (curvature) based on direct position measurements of the plant, and does not rely upon modeling the plant response to persistent excitation. The system and method can be naturally applied in various applications in which plant performance functions have multiple independent parameters, and it does not depend upon frequency separation to distinguish between system dimensions.

Efficient computation of optimal actions.

PubMed

Todorov, Emanuel

2009-07-14

Optimal choice of actions is a fundamental problem relevant to fields as diverse as neuroscience, psychology, economics, computer science, and control engineering. Despite this broad relevance the abstract setting is similar: we have an agent choosing actions over time, an uncertain dynamical system whose state is affected by those actions, and a performance criterion that the agent seeks to optimize. Solving problems of this kind remains hard, in part, because of overly generic formulations. Here, we propose a more structured formulation that greatly simplifies the construction of optimal control laws in both discrete and continuous domains. An exhaustive search over actions is avoided and the problem becomes linear. This yields algorithms that outperform Dynamic Programming and Reinforcement Learning, and thereby solve traditional problems more efficiently. Our framework also enables computations that were not possible before: composing optimal control laws by mixing primitives, applying deterministic methods to stochastic systems, quantifying the benefits of error tolerance, and inferring goals from behavioral data via convex optimization. Development of a general class of easily solvable problems tends to accelerate progress--as linear systems theory has done, for example. Our framework may have similar impact in fields where optimal choice of actions is relevant.

Proximity Navigation of Highly Constrained Spacecraft

NASA Technical Reports Server (NTRS)

Scarritt, S.; Swartwout, M.

2007-01-01

Bandit is a 3-kg automated spacecraft in development at Washington University in St. Louis. Bandit's primary mission is to demonstrate proximity navigation, including docking, around a 25-kg student-built host spacecraft. However, because of extreme constraints in mass, power and volume, traditional sensing and actuation methods are not available. In particular, Bandit carries only 8 fixed-magnitude cold-gas thrusters to control its 6 DOF motion. Bandit lacks true inertial sensing, and the ability to sense position relative to the host has error bounds that approach the size of the Bandit itself. Some of the navigation problems are addressed through an extremely robust, error-tolerant soft dock. In addition, we have identified a control methodology that performs well in this constrained environment: behavior-based velocity potential functions, which use a minimum-seeking method similar to Lyapunov functions. We have also adapted the discrete Kalman filter for use on Bandit for position estimation and have developed a similar measurement vs. propagation weighting algorithm for attitude estimation. This paper provides an overview of Bandit and describes the control and estimation approach. Results using our 6DOF flight simulator are provided, demonstrating that these methods show promise for flight use.

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

A Goal Seeking Strategy for Constructing Systems from Alternative Components

NASA Technical Reports Server (NTRS)

Valentine, Mark E.

1999-01-01

This paper describes a methodology to efficiently construct feasible systems then modify feasible systems to meet successive goals by selecting from alternative components, a problem recognized to be n-p complete. The methodology provides a means to catalog and model alternative components. A presented system modeling Structure is robust enough to model a wide variety of systems and provides a means to compare and evaluate alternative systems. These models act as input to a methodology for selecting alternative components to construct feasible systems and modify feasible systems to meet design goals and objectives. The presented algorithm's ability to find a restricted solution, as defined by a unique set of requirements, is demonstrated against an exhaustive search of a sample of proposed shuttle modifications. The utility of the algorithm is demonstrated by comparing results from the algorithm with results from three NASA shuttle evolution studies using their value systems and assumptions.

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Efficient Mean Field Variational Algorithm for Data Assimilation (Invited)

NASA Astrophysics Data System (ADS)

Vrettas, M. D.; Cornford, D.; Opper, M.

2013-12-01

Data assimilation algorithms combine available observations of physical systems with the assumed model dynamics in a systematic manner, to produce better estimates of initial conditions for prediction. Broadly they can be categorized in three main approaches: (a) sequential algorithms, (b) sampling methods and (c) variational algorithms which transform the density estimation problem to an optimization problem. However, given finite computational resources, only a handful of ensemble Kalman filters and 4DVar algorithms have been applied operationally to very high dimensional geophysical applications, such as weather forecasting. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the ';optimal' posterior distribution over the continuous time states, within a family of non-stationary Gaussian processes. Our initial work on variational Bayesian approaches to data assimilation, unlike the well-known 4DVar method which seeks only the most probable solution, computes the best time varying Gaussian process approximation to the posterior smoothing distribution for dynamical systems that can be represented by stochastic differential equations. This approach was based on minimising the Kullback-Leibler divergence, over paths, between the true posterior and our Gaussian process approximation. Whilst the observations were informative enough to keep the posterior smoothing density close to Gaussian the algorithm proved very effective on low dimensional systems (e.g. O(10)D). However for higher dimensional systems, the high computational demands make the algorithm prohibitively expensive. To overcome the difficulties presented in the original framework and make our approach more efficient in higher dimensional systems we have been developing a new mean field version of the algorithm which treats the state variables at any given time as being independent in the posterior approximation, while still accounting for their relationships in the mean solution arising from the original system dynamics. Here we present this new mean field approach, illustrating its performance on a range of benchmark data assimilation problems whose dimensionality varies from O(10) to O(10^3)D. We emphasise that the variational Bayesian approach we adopt, unlike other variational approaches, provides a natural bound on the marginal likelihood of the observations given the model parameters which also allows for inference of (hyper-) parameters such as observational errors, parameters in the dynamical model and model error representation. We also stress that since our approach is intrinsically parallel it can be implemented very efficiently to address very long data assimilation time windows. Moreover, like most traditional variational approaches our Bayesian variational method has the benefit of being posed as an optimisation problem therefore its complexity can be tuned to the available computational resources. We finish with a sketch of possible future directions.

Personality trait interactions in parents of patients with borderline personality disorder: a controlled study using the Temperament and Character Inventory.

PubMed

Fassino, Secondo; Amianto, Federico; Gastaldi, Filippo; Abbate-Daga, Giovanni; Brambilla, Francesca; Leombruni, Paolo

2009-01-30

Family environment is a pathogenic factor of borderline personality disorder (BPD). However, the personality traits of patients with BPD and their parents have never been assessed using the same instrument and then examined for relationships. In the present study, we explored the temperament and character traits of BPD patients and their parents to investigate possible interactions. In total, 56 patients with BPD and their parents were evaluated with the Temperament and Character Inventory (TCI) and compared with 53 control families. Discriminant and correlation analyses indicated that subjects with BPD displayed higher levels of novelty seeking, harm avoidance, and self-transcendence and lower levels of self-directedness than control subjects. Their fathers displayed higher levels of novelty seeking and lower levels of persistence and self-directedness, and their mothers displayed lower levels of self-directedness compared with levels in control parents. In BPD families, temperament and character traits displayed high levels of discriminatory power. Novelty seeking in offspring with borderline personality disorder was significantly correlated with their mothers' novelty seeking and their fathers' self-transcendence. Self-directedness in borderline offspring was significantly correlated with both their mothers' and fathers' novelty seeking, and their self-transcendence was significantly correlated with their mothers' novelty seeking and harm avoidance. The different correlational pattern for borderline and control families is discussed. Characteristic personality patterns were found in BPD offspring and in both parents. The relationship between personality traits of borderline offspring and those of their parents may be related to both genetic transmission and family dynamics. Ramifications for treatment are discussed.

Fuzzy decoupling controller based on multimode control algorithm of PI-single neuron and its application

NASA Astrophysics Data System (ADS)

Zhang, Xianxia; Wang, Jian; Qin, Tinggao

2003-09-01

Intelligent control algorithms are introduced into the control system of temperature and humidity. A multi-mode control algorithm of PI-Single Neuron is proposed for single loop control of temperature and humidity. In order to remove the coupling between temperature and humidity, a new decoupling method is presented, which is called fuzzy decoupling. The decoupling is achieved by using a fuzzy controller that dynamically modifies the static decoupling coefficient. Taking the control algorithm of PI-Single Neuron as the single loop control of temperature and humidity, the paper provides the simulated output response curves with no decoupling control, static decoupling control and fuzzy decoupling control. Those control algorithms are easily implemented in singlechip-based hardware systems.

Optimizing Tissue Sampling for the Diagnosis, Subtyping, and Molecular Analysis of Lung Cancer

PubMed Central

Ofiara, Linda Marie; Navasakulpong, Asma; Beaudoin, Stephane; Gonzalez, Anne Valerie

2014-01-01

Lung cancer has entered the era of personalized therapy with histologic subclassification and the presence of molecular biomarkers becoming increasingly important in therapeutic algorithms. At the same time, biopsy specimens are becoming increasingly smaller as diagnostic algorithms seek to establish diagnosis and stage with the least invasive techniques. Here, we review techniques used in the diagnosis of lung cancer including bronchoscopy, ultrasound-guided bronchoscopy, transthoracic needle biopsy, and thoracoscopy. In addition to discussing indications and complications, we focus our discussion on diagnostic yields and the feasibility of testing for molecular biomarkers such as epidermal growth factor receptor and anaplastic lymphoma kinase, emphasizing the importance of a sufficient tumor biopsy. PMID:25295226

Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

PubMed

Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Conflict-Aware Scheduling Algorithm

NASA Technical Reports Server (NTRS)

Wang, Yeou-Fang; Borden, Chester

2006-01-01

conflict-aware scheduling algorithm is being developed to help automate the allocation of NASA s Deep Space Network (DSN) antennas and equipment that are used to communicate with interplanetary scientific spacecraft. The current approach for scheduling DSN ground resources seeks to provide an equitable distribution of tracking services among the multiple scientific missions and is very labor intensive. Due to the large (and increasing) number of mission requests for DSN services, combined with technical and geometric constraints, the DSN is highly oversubscribed. To help automate the process, and reduce the DSN and spaceflight project labor effort required for initiating, maintaining, and negotiating schedules, a new scheduling algorithm is being developed. The scheduling algorithm generates a "conflict-aware" schedule, where all requests are scheduled based on a dynamic priority scheme. The conflict-aware scheduling algorithm allocates all requests for DSN tracking services while identifying and maintaining the conflicts to facilitate collaboration and negotiation between spaceflight missions. These contrast with traditional "conflict-free" scheduling algorithms that assign tracks that are not in conflict and mark the remainder as unscheduled. In the case where full schedule automation is desired (based on mission/event priorities, fairness, allocation rules, geometric constraints, and ground system capabilities/ constraints), a conflict-free schedule can easily be created from the conflict-aware schedule by removing lower priority items that are in conflict.

Autonomous & Adaptive Oceanographic Feature Tracking on Board Autonomous Underwater Vehicles

DTIC Science & Technology

2015-02-01

44 3.6 Tracking the Marine ermocline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.6.1 ermocline Definition ...intelligent autonomy algorithms to adapt the vehicle’s motion to changes in the environment, effectively seeking out and tracking an oceanographic...interface, H is the mean water depth, and f is the Coriolis parameter (twice the earth’s angular velocity about its vertical axis) [38]. at is, the

Pattern recognition for Space Applications Center director's discretionary fund

NASA Technical Reports Server (NTRS)

Singley, M. E.

1984-01-01

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

DoD Research and Engineering Enterprise

DTIC Science & Technology

2014-05-01

Secretary of Defense Hagel, Pentagon Press Briefing Room, February 24, 2014 Technological superiority has been central to the strategy of the...understand the environment, to software algorithms that can make a decision or seek human assistance. Through autonomy, we should be able to greatly reduce...computers are a commercial product 1 , and quantum key distribution for data encryption is nearly a commercial product. These two applications are

How To Use the Spreadsheet as a Tool in the Secondary School Mathematics Classroom. Second Edition (for Windows and Macintosh Operating Systems).

ERIC Educational Resources Information Center

Masalski, William J.

This book seeks to develop, enhance, and expand students' understanding of mathematics by using technology. Topics covered include the advantages of spreadsheets along with the opportunity to explore the 'what if?' type of questions encountered in the problem-solving process, enhancing the user's insight into the development and use of algorithms,…

Control of equipment isolation system using wavelet-based hybrid sliding mode control

NASA Astrophysics Data System (ADS)

Huang, Shieh-Kung; Loh, Chin-Hsiung

2017-04-01

Critical non-structural equipment, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., is vulnerable to strong earthquakes, and on top of that, the failure of the vibration-sensitive equipment will cause severe economic loss. In order to protect vibration-sensitive equipment or machinery against strong earthquakes, various innovative control algorithms are developed to compensate the internal forces that to be applied. These new or improved control strategies, such as the control algorithms based on optimal control theory and sliding mode control (SMC), are also developed for structures engineering as a key element in smart structure technology. The optimal control theory, one of the most common methodologies in feedback control, finds control forces through achieving a certain optimal criterion by minimizing a cost function. For example, the linear-quadratic regulator (LQR) was the most popular control algorithm over the past three decades, and a number of modifications have been proposed to increase the efficiency of classical LQR algorithm. However, except to the advantage of simplicity and ease of implementation, LQR are susceptible to parameter uncertainty and modeling error due to complex nature of civil structures. Different from LQR control, a robust and easy to be implemented control algorithm, SMC has also been studied. SMC is a nonlinear control methodology that forces the structural system to slide along surfaces or boundaries; hence this control algorithm is naturally robust with respect to parametric uncertainties of a structure. Early attempts at protecting vibration-sensitive equipment were based on the use of existing control algorithms as described above. However, in recent years, researchers have tried to renew the existing control algorithms or developing a new control algorithm to adapt the complex nature of civil structures which include the control of both structures and non-structural components. The aim of this paper is to develop a hybrid control algorithm on the control of both structures and equipments simultaneously to overcome the limitations of classical feedback control through combining the advantage of classic LQR and SMC. To suppress vibrations with the frequency contents of strong earthquakes differing from the natural frequencies of civil structures, the hybrid control algorithms integrated with the wavelet-base vibration control algorithm is developed. The performance of classical, hybrid, and wavelet-based hybrid control algorithms as well as the responses of structure and non-structural components are evaluated and discussed through numerical simulation in this study.

Stress-buffering Effect of Coping Strategies on Interrole Conflict among Family Caregivers of People with Dementia.

PubMed

Morimoto, Hiroshi; Furuta, Nobuo; Kono, Mitsue; Kabeya, Mayumi

2017-08-23

To examine the stress-buffering effect of coping strategies on the adverse effects of interrole conflict on the mental health of employed family caregivers, and clarify the moderating role of attentional control on this stress-buffering effect. Data were drawn from a two-wave longitudinal online survey of employed Japanese family caregivers of people with dementia (263 males, 116 females; age 51.54 ± 9.07 years). We assessed interrole conflict, coping strategies, attentional control, mental health variables (psychological strain and quality of life), and confounding factors. Hierarchical regression analyses controlled for sociodemographic factors found formal support seeking had a stress-buffering effect for strain- and behavior-based caregiving interfering with work (CIW) only on psychological strain, and was moderated by attentional control. Single slope analysis showed higher CIW was related to higher psychological strain in those with greater use of formal support seeking and lower attentional control, but not in those with higher attentional control. Greater use of formal support seeking weakens the adverse effects of strain- and behavior-based CIW on psychological strain in people with high attentional control. Attentional control is a key factor in the stress-buffering effect of formal support seeking on strain- and behavior-based CIW.

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

PubMed

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

Development of model reference adaptive control theory for electric power plant control applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mabius, L.E.

1982-09-15

The scope of this effort includes the theoretical development of a multi-input, multi-output (MIMO) Model Reference Control (MRC) algorithm, (i.e., model following control law), Model Reference Adaptive Control (MRAC) algorithm and the formulation of a nonlinear model of a typical electric power plant. Previous single-input, single-output MRAC algorithm designs have been generalized to MIMO MRAC designs using the MIMO MRC algorithm. This MRC algorithm, which has been developed using Command Generator Tracker methodologies, represents the steady state behavior (in the adaptive sense) of the MRAC algorithm. The MRC algorithm is a fundamental component in the MRAC design and stability analysis.more » An enhanced MRC algorithm, which has been developed for systems with more controls than regulated outputs, alleviates the MRC stability constraint of stable plant transmission zeroes. The nonlinear power plant model is based on the Cromby model with the addition of a governor valve management algorithm, turbine dynamics and turbine interactions with extraction flows. An application of the MRC algorithm to a linearization of this model demonstrates its applicability to power plant systems. In particular, the generated power changes at 7% per minute while throttle pressure and temperature, reheat temperature and drum level are held constant with a reasonable level of control. The enhanced algorithm reduces significantly control fluctuations without modifying the output response.« less

Seeking out SARI: an automated search of electronic health records.

PubMed

O'Horo, John C; Dziadzko, Mikhail; Sakusic, Amra; Ali, Rashid; Sohail, M Rizwan; Kor, Daryl J; Gajic, Ognjen

2018-06-01

The definition of severe acute respiratory infection (SARI) - a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission - has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

Understanding help-seeking intentions in male military cadets: An application of perceptual mapping.

PubMed

Bass, Sarah Bauerle; Muñiz, Javier; Gordon, Thomas F; Maurer, Laurie; Patterson, Freda

2016-05-17

Research suggests that men are less likely to seek help for depression, substance abuse, and stressful life events due to negative perceptions of asking for and receiving help. This may be exacerbated in male military cadets who exhibit higher levels of gender role conflict because of military culture. This exploratory study examined the perceptions of 78 male military cadets toward help-seeking behaviors. Cadets completed the 31-item Barriers to Help Seeking Scale (BHSS) and a component factor analysis was used to generate five composite variables and compare to validated factors. Perceptual mapping and vector modeling, which produce 3-dimensional models of a group's perceptions, were then used to model how they conceptualize help-seeking. Factor analysis showed slightly different groupings than the BHSS, perhaps attributed to different characteristics of respondents, who are situated in a military school compared to general university males. Perceptual maps show that cadets perceive trust of doctors closest to them and help-seeking farthest, supporting the concept that these males have rigid beliefs about having control and its relationship to health seeking. Differences were seen when comparing maps of White and non-White cadets. White cadets positioned themselves far away from all variables, while non-White cadets were closest to "emotional control". To move these cadets toward help-seeking, vector modeling suggests that interventions should focus on their general trust of doctors, accepting lack of control, and decreasing feelings of weakness when asking for help. For non-White cadets a focus on self-reliance may also need to be emphasized. Use of these unique methods resulted in articulation of specific barriers that if addressed early, may have lasting effects on help-seeking behavior as these young men become adults. Future studies are needed to develop and test specific interventions to promote help-seeking among military cadets.

Intact goal-directed control in treatment-seeking drug users indexed by outcome-devaluation and Pavlovian to instrumental transfer: Critique of habit theory.

PubMed

Hogarth, Lee; Lam-Cassettari, Christa; Pacitti, Helena; Currah, Tara; Mahlberg, Justin; Hartley, Lucie; Moustafa, Ahmed

2018-05-22

Animal studies have demonstrated that chronic exposure to drugs of abuse impairs goal-directed control over action selection indexed by the outcome-devaluation and specific Pavlovian to instrumental transfer procedures, suggesting this impairment might underpin addiction. However, there is currently only weak evidence for impaired goal-directed control in human drug users. Two experiments were undertaken in which treatment-seeking drug users and non-matched normative reference samples (controls) completed outcome-devaluation and specific Pavlovian to instrumental transfer procedures notionally translatable to animal procedures (Experiment 2 used a more challenging biconditional schedule). The two experiments found significant outcome-devaluation and specific Pavlovian to instrumental transfer effects overall and there was no significant difference between groups in the magnitude of these effects. Moreover, Bayes factor supported the null hypothesis for these group comparisons. Although limited by non-matched group comparisons and small sample sizes, the two studies suggest that treatment-seeking drug users have intact goal-directed control over action selection, adding uncertainty to already mixed evidence concerning the role of habit learning in human drug dependence. Neuro-interventions might seek to tackle goal-directed drug-seeking rather than habit formation in drug users. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Intention to Seek Mental Health Treatment for Adolescent Girls: Comparison of Predictors in Mothers and Daughters

PubMed Central

Logsdon, M. Cynthia; Pinto, Melissa D.; LaJoie, A. Scott; Hertweck, Paige; Lynch, Tania; Flamini, Laura

2013-01-01

PROBLEM To examine predictors of intention to seek mental health treatment for adolescent girls in mothers and daughters. METHODS In this cross-sectional study, mothers and adolescent daughters (n = 71) completed measures of behavioral attitudes, subjective norms, perceived behavioral control, and intention to seek mental health treatment for the adolescent daughter. FINDINGS Behavioral attitude and perceived behavioral control predicted intention to seek mental health treatment among mothers. Behavioral attitude predicted intention among daughters. There were no associations between mothers and daughters on study variables. CONCLUSIONS To promote shared decision making and engagement in mental health treatment, clinicians may target interventions to the mother’s perceived behavioral control and behavioral attitudes of daughters and mothers. Based upon study results, clinicians should promote shared decision making and concordance between mothers and daughters on attitudes toward mental health treatment. PMID:24180603

D4 dopamine-receptor (DRD4) alleles and novelty seeking in substance-dependent, personality-disorder, and control subjects.

PubMed Central

Gelernter, J; Kranzler, H; Coccaro, E; Siever, L; New, A; Mulgrew, C L

1997-01-01

Two reports have been published suggesting an association between the personality trait of novelty seeking and the DRD4*7R allele at the D4 dopamine-receptor locus (with heterozygotes or homozygotes for DRD4*7R having higher novelty seeking). We studied novelty seeking and four coding-sequence polymorphisms affecting protein structure in the D4 dopamine-receptor gene (DRD4) in a sample of 341 American subjects, of whom 224 are of primarily European ancestry and 117 are of primarily African ancestry. These subjects had diagnoses of substance dependence or personality disorder (PD) or were screened to exclude major psychiatric diagnosis. We found that, although the substance-dependent subjects had significantly higher novelty seeking than the control and PD subjects, they did not differ in DRD4*7R allele frequency. There was no association between any DRD4 polymorphism and novelty seeking in any population or diagnostic group, except for a significant association between the DRD4*7R allele and lower novelty seeking among European American females and African American substance abusers. The novelty seeking of subjects heterozygous for a null mutation did not differ from that of subjects with two functional alleles. We conclude that the most likely explanation of these results is that the DRD4 VNTR does not influence directly the trait of novelty seeking, in these samples. PMID:9345090

Sensation seeking and smoking behaviors among adolescents in the Republic of Korea.

PubMed

Hwang, Heejin; Park, Sunhee

2015-06-01

This study aimed to explore the relationship between the four components of sensation seeking (i.e., disinhibition, thrill and adventure seeking, experience seeking, and boredom susceptibility) and three types of smoking behavior (i.e., non-smoking, experimental smoking, and current smoking) among high school students in the Republic of Korea. Multivariate multinomial logistic regression analysis was performed using two models. In Model 1, the four subscales of sensation seeking were used as covariates, and in Model 2, other control factors (i.e., characteristics related to demographics, individuals, family, school, and friends) were added to Model 1 in order to adjust for their effects. In Model 1, the impact of disinhibition on experimental smoking and current smoking was statistically significant. In Model 2, the influence of disinhibition on both of these smoking behaviors remained statistically significant after controlling for all the other covariates. Also, the effect of thrill and adventure seeking on experimental smoking was statistically significant. The two statistically significant subscales of sensation seeking were positively associated with the risk of smoking behaviors. According to extant literature and current research, sensation seeking, particularly disinhibition, is strongly associated with smoking among youth. Therefore, sensation seeking should be measured among adolescents to identify those who are at greater risk of smoking and to develop more effective intervention strategies in order to curb the smoking epidemic among youth. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictors of treatment seeking intention among people with cough in East Wollega, Ethiopia based on the theory of planned behavior: a community based cross-sectional study.

PubMed

Addisu, Yohannes; Birhanu, Zewdie; Tilahun, Dejen; Assefa, Tsion

2014-04-01

Early treatment seeking for cough is crucial in the prevention and control of Tuberculosis. This study was intended to assess treatment seeking intention of people with cough of more than two weeks, and to identify its predictors. A community based cross-sectional study was conducted among 763 individuals with cough of more than two weeks in East Wollega Zone from March 10 to April 16, 2011. Study participants were selected from eighteen villages by cluster sampling method. Data collection instruments were developed according to the standard guideline of the theory of planned behavior. The data were analyzed with SPSS 16.0. Multiple linear regression was used to identify predictors. Mean score of intention was found to be 12.6 (SD=2.8) (range of possible score=3-15). Knowledge (β=0.14, 95%CI: 0.07-0.2), direct attitude (β=0.31, 95%CI: 0.25-0.35), belief-based attitude (β=0.03, 95%CI: 0.02-0.06) and perceived subjective norm (β=0.22, 95%CI: 0.13-0.31) positively predicted treatment seeking intention. However, perceived behavioral control and control belief were not significantly associated with treatment seeking intention (p>0.05). Being smoker (β=-0.97, 95%CI:-1.65 (-0.37)) and higher family income (β=-0.06, 95%CI:-0.07-(-0.01) were significantly associated with lower treatment seeking intention. TPB significantly predicted treatment seeking intention among the study participants. Attitude and silent beliefs held by the respondents play an important role and should be given emphasize in prevention and control of Tuberculosis.

The Effects of the Monoamine Stabilizer (-)-OSU6162 on Binge-Like Eating and Cue-Controlled Food-Seeking Behavior in Rats.

PubMed

Feltmann, Kristin; Giuliano, Chiara; Everitt, Barry J; Steensland, Pia; Alsiö, Johan

2018-02-01

Binge-eating disorder (BED) is characterized by recurring episodes of excessive consumption of palatable food and an increased sensitivity to food cues. Patients with BED display an addiction-like symptomatology and the dopamine system might be a potential treatment target. The clinically safe monoamine stabilizer (-)-OSU6162 (OSU6162) restores dopaminergic dysfunction in long-term alcohol-drinking rats and shows promise as a novel treatment for alcohol use disorder. Here, the effects of OSU6162 on consummatory (binge-like eating) and appetitive (cue-controlled seeking) behavior motivated by chocolate-flavored sucrose pellets were evaluated in non-food-restricted male Lister Hooded rats. OSU6162 significantly reduced binge-like intake of chocolate-flavored sucrose pellets without affecting prior chow intake. Furthermore, OSU6162 significantly reduced the cue-controlled seeking of chocolate-flavored sucrose pellets under a second-order schedule of reinforcement before, but not after, the delivery and ingestion of reward, indicating a selective effect on incentive motivational processes. In contrast, the dopamine D2/D3 receptor antagonist raclopride reduced the seeking of chocolate-flavored sucrose pellets both pre- and post reward ingestion and also reduced responding under simpler schedules of seeking behavior. The D1/5 receptor antagonist SCH23390 had no effect on instrumental behavior under any reinforcement schedule tested. Finally, local administration of OSU6162 into the nucleus accumbens core, but not dorsolateral striatum, selectively reduced cue-controlled sucrose seeking. In conclusion, the present results show that OSU6162 reduces binge-like eating behavior and attenuates the impact of cues on seeking of palatable food. This indicates that OSU6162 might serve as a novel BED medication.

Factors driving public tolerance levels and information-seeking behaviour concerning insects in the household environment.

PubMed

Schoelitsz, Bruce; Poortvliet, P Marijn; Takken, Willem

2018-06-01

The public's negative attitudes towards household insects drive tolerance for these insects and their control. Tolerance levels are important in integrated pest management (IPM), as are pest knowledge and information. The risk information seeking and processing (RISP) model describes the relationships between personal factors and information-seeking behaviour. We combined IPM and RISP to determine important relationships between factors driving insect tolerance levels and information-seeking behaviour through an online survey and tested whether this model is valid and generally applicable. Relationships between variables from both IPM and RISP models were tested for seven insect species. Tolerance levels were measured with two factors: willingness to pay for pest control and whether insects are tolerated. Willingness to pay for control was positively affected by age, experience, risk perception, insect characteristics, and negative emotions and affected behavioural intention, by influencing information sufficiency and information-seeking behaviour. Tolerability was influenced by perception of insect characteristics and determines whether control measures are taken. It was possible to combine the RISP and IPM models. Relevant driving factors were a person's age, experience, risk perception, negative affective responses, tolerance levels, relevant channel beliefs about online forums, information sufficiency and information-seeking behaviour. There was, however, variation in important factors between different insects. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Demographic, Socioeconomic, and Geographic Factors Leading to Severe Malaria and Delayed Care Seeking in Ugandan Children: A Case-Control Study.

PubMed

Mpimbaza, Arthur; Ndeezi, Grace; Katahoire, Anne; Rosenthal, Philip J; Karamagi, Charles

2017-11-01

We studied associations between delayed care seeking, demographic, socioeconomic, and geographic factors and likelihood of severe malaria in Ugandan children. The study was based at Jinja Hospital, Uganda. We enrolled 325 severe malaria cases and 325 uncomplicated malaria controls matched by age and residence. Patient details, an itinerary of events in response to illness, household information, and location of participants' residences were captured. Conditional logistic regression was used to determine risk factors for severe malaria and delayed care seeking. Delayed care seeking (≥ 24 hours after fever onset; odds ratio [OR] 5.50; 95% confidence interval [CI] 2.70, 11.1), seeking care at a drug shop as the initial response to illness (OR 3.62; 95% CI 1.86, 7.03), and increasing distance from place of residence to the nearest health center (OR 1.45; 95% CI 1.17, 1.79) were independent risk factors for severe malaria. On subgroup analysis, delayed care seeking was a significant risk factor in children with severe malaria attributable to severe anemia (OR 15.6; 95% CI 3.02, 80.6), but not unconsciousness (OR 1.13; 95% CI 0.30, 4.28). Seeking care at a drug shop (OR 2.84; 95% CI 1.12, 7.21) and increasing distance to the nearest health center (OR 1.18; 95% CI 1.01, 1.37) were independent risk factors for delayed care seeking. Delayed care seeking and seeking care at a drug shop were risk factors for severe malaria. Seeking care at a drug shop was also a predictor of delayed care seeking. The role of drug shops in contributing to delayed care and risk of severe malaria requires further study.

A Re-Examination of Neuropsychological Functioning in Persian Gulf War Era Veterans

DTIC Science & Technology

2003-08-01

neuropsychological evaluations and a group of individuals seeking treatment or diagnostic evaluation for any purpose. Controls were treatment -seeking non deployed...GW-era veterans studied between 1995-1998. The prior finding of differences between the deployed and non-deployed treatment seeking GW-era veterans in...included patients who were initially referred for clinical neuropsychological evaluations and a group of individuals who were seeking treatment or

Looking beyond the Internet: examining socioeconomic inequalities in cancer information seeking among cancer patients.

PubMed

Lee, Chul-Joo; Ramírez, A Susana; Lewis, Nehama; Gray, Stacy W; Hornik, Robert C

2012-01-01

The gap in cancer information seeking between high-socioeconomic-status (high-SES) cancer patients and low-SES cancer patients deserves serious attention, considering the importance of information and knowledge in cancer control. We thus explored the association of SES, as measured by education, with cancer patients' overall cancer information seeking, and with seeking from each source (i.e., the Internet, mass media, medical sources, and nonmedical interpersonal sources) and across two topic categories (i.e., treatment, quality of life). We then asked whether the effect of education on treatment information seeking is reduced among those who are particularly motivated to control treatment choices. We conducted a survey with breast, prostate, and colon cancer patients diagnosed in 2005 (n = 2,013), who were randomly drawn from the Pennsylvania Cancer Registry in the fall of 2006. We found that education was more strongly associated with Internet use than with the use of other sources regardless of topics. Also, when information was sought from mass media, education had a greater association with treatment information seeking than with quality-of-life information seeking. Preference for active participation in treatment decision making, however, did not moderate the effect of education on treatment information seeking. The implications of these findings for public health research and cancer patient education were discussed.

Sensation seeking, peer deviance, and genetic influences on adolescent delinquency: Evidence for person-environment correlation and interaction.

PubMed

Mann, Frank D; Patterson, Megan W; Grotzinger, Andrew D; Kretsch, Natalie; Tackett, Jennifer L; Tucker-Drob, Elliot M; Harden, K Paige

2016-07-01

Both sensation seeking and affiliation with deviant peer groups are risk factors for delinquency in adolescence. In this study, we use a sample of adolescent twins (n = 549), 13 to 20 years old (M age = 15.8 years), in order to test the interactive effects of peer deviance and sensation seeking on delinquency in a genetically informative design. Consistent with a socialization effect, affiliation with deviant peers was associated with higher delinquency even after controlling for selection effects using a co-twin-control comparison. At the same time, there was evidence for person-environment correlation; adolescents with genetic dispositions toward higher sensation seeking were more likely to report having deviant peer groups. Genetic influences on sensation seeking substantially overlapped with genetic influences on adolescent delinquency. Finally, the environmentally mediated effect of peer deviance on adolescent delinquency was moderated by individual differences in sensation seeking. Adolescents reporting high levels of sensation seeking were more susceptible to deviant peers, a Person × Environment interaction. These results are consistent with both selection and socialization processes in adolescent peer relationships, and they highlight the role of sensation seeking as an intermediary phenotype for genetic risk for delinquency. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Looking beyond the Internet: Examining Socioeconomic Inequalities in Cancer Information Seeking among Cancer Patients

PubMed Central

Lee, Chul-joo; Ramirez, Susana; Lewis, Nehama; Gray, Stacy W.; Hornik, Robert C.

2014-01-01

The gap in cancer information seeking between high-socioeconomic status (SES) cancer patients and low-SES cancer patients deserves serious attention considering the importance of information and knowledge in cancer control. We thus explored the association of SES, as measured by education, with cancer patients’ overall cancer information seeking, and with seeking from each source (i.e., the Internet, mass media, medical sources, and non-medical interpersonal sources) and across two topic categories (i.e., treatment, quality of life). We then asked whether the effect of education on treatment information seeking is reduced among those who are particularly motivated to control treatment choices. We conducted a survey with breast, prostate, and colon cancer patients diagnosed in 2005 (N = 2,013), who were randomly drawn from the Pennsylvania Cancer Registry in the fall of 2006. We found that education was more strongly associated with Internet use than with the use of other sources regardless of topics. Also, when information was sought from mass media, education had a greater association with treatment information seeking than with quality-of-life information seeking. Preference for active participation in treatment decision making, however, did not moderate the effect of education on treatment information seeking. The implications of these findings for public health research and cancer patient education were discussed. PMID:22356137

Research on intelligent algorithm of electro - hydraulic servo control system

NASA Astrophysics Data System (ADS)

Wang, Yannian; Zhao, Yuhui; Liu, Chengtao

2017-09-01

In order to adapt the nonlinear characteristics of the electro-hydraulic servo control system and the influence of complex interference in the industrial field, using a fuzzy PID switching learning algorithm is proposed and a fuzzy PID switching learning controller is designed and applied in the electro-hydraulic servo controller. The designed controller not only combines the advantages of the fuzzy control and PID control, but also introduces the learning algorithm into the switching function, which makes the learning of the three parameters in the switching function can avoid the instability of the system during the switching between the fuzzy control and PID control algorithms. It also makes the switch between these two control algorithm more smoother than that of the conventional fuzzy PID.

Study of Barrier to Help Seeking and its Relationships with Disability in Patients with Headache

PubMed Central

John, Deepa; Sundarmurthy, Harsha; Rathod, Harshal; Rathod, Snehal

2016-01-01

Introduction Headache is among the first three most prevalent disorders with a wide treatment gap due to barriers in help seeking. Headache has been associated with disability. However, the relationship of barriers to help-seeking and disability are unexplored. Aim To find out the barriers to help seeking and its relationship with headache related disability in patients with headache. Materials and Methods In this hospital based cross-sectional study, 200 consecutive subjects with headache attending a tertiary care centre were recruited as per selection criteria and assessed with Sociodemographic & Clinical Proforma, Mini International Neuropsychiatric Interview (MINI), Barriers to Help Seeking Scale (BHSS), The Henry Ford Hospital Headache Disability Inventory (HDI). Results High mean score was observed on BHSS subscale need for control and self reliance (19.45; SD ±9.66) and minimizing problem and resignation (10.02; SD ±6.98). Mean score on the HDI was 25.65 (SD ± 14.09). Socioeconomic status of the patient was statistically significant and positively associated with need for control and self reliance (p=0.035), concrete barriers and distrust of care givers (p=0.039), emotional control (p=0.005), and privacy (p=0.002). Occupational status had significant association with need for control and self-reliance (p=0.01), minimizing problem and resignation (p=0.033), and emotional control (p=0.006). Score on hospital headache disability inventory significantly predicted the value of score on concrete barriers and distrust of caregivers domain of HDI (p=0.001). Conclusion Autonomy and under estimation of seriousness of headache are common barriers to help seeking. Pattern of help seeking barriers may vary with socio-economic status and occupational status, while disability varies with gender and severity of headache. Headache associated disability is positively associated with concrete barriers. PMID:27891430

Asian International Students' Willingness to Seek Counseling: A Mixed-Methods Study

ERIC Educational Resources Information Center

Li, Peiwei; Wong, Y. Joel; Toth, Paul

2013-01-01

Using a mixed-methods survey design that was predominantly quantitative, this study explored Asian international students' willingness to seek counseling. Participants were 177 Asian international students recruited from a U.S. Midwestern University. After controlling for attitudes toward psychological help-seeking and past counseling experience,…

Boiler-turbine control system design using a genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

A comparison of force control algorithms for robots in contact with flexible environments

NASA Technical Reports Server (NTRS)

Wilfinger, Lee S.

1992-01-01

In order to perform useful tasks, the robot end-effector must come into contact with its environment. For such tasks, force feedback is frequently used to control the interaction forces. Control of these forces is complicated by the fact that the flexibility of the environment affects the stability of the force control algorithm. Because of the wide variety of different materials present in everyday environments, it is necessary to gain an understanding of how environmental flexibility affects the stability of force control algorithms. This report presents the theory and experimental results of two force control algorithms: Position Accommodation Control and Direct Force Servoing. The implementation of each of these algorithms on a two-arm robotic test bed located in the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) is discussed in detail. The behavior of each algorithm when contacting materials of different flexibility is experimentally determined. In addition, several robustness improvements to the Direct Force Servoing algorithm are suggested and experimentally verified. Finally, a qualitative comparison of the force control algorithms is provided, along with a description of a general tuning process for each control method.

Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

PubMed

Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

2015-09-01

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Fast-match on particle swarm optimization with variant system mechanism

NASA Astrophysics Data System (ADS)

Wang, Yuehuang; Fang, Xin; Chen, Jie

2018-03-01

Fast-Match is a fast and effective algorithm for approximate template matching under 2D affine transformations, which can match the target with maximum similarity without knowing the target gesture. It depends on the minimum Sum-of-Absolute-Differences (SAD) error to obtain the best affine transformation. The algorithm is widely used in the field of matching images because of its fastness and robustness. In this paper, our approach is to search an approximate affine transformation over Particle Swarm Optimization (PSO) algorithm. We treat each potential transformation as a particle that possesses memory function. Each particle is given a random speed and flows throughout the 2D affine transformation space. To accelerate the algorithm and improve the abilities of seeking the global excellent result, we have introduced the variant system mechanism on this basis. The benefit is that we can avoid matching with huge amount of potential transformations and falling into local optimal condition, so that we can use a few transformations to approximate the optimal solution. The experimental results prove that our method has a faster speed and a higher accuracy performance with smaller affine transformation space.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

Merging Digital Medicine and Economics: Two Moving Averages Unlock Biosignals for Better Health.

PubMed

Elgendi, Mohamed

2018-01-06

Algorithm development in digital medicine necessitates ongoing knowledge and skills updating to match the current demands and constant progression in the field. In today's chaotic world there is an increasing trend to seek out simple solutions for complex problems that can increase efficiency, reduce resource consumption, and improve scalability. This desire has spilled over into the world of science and research where many disciplines have taken to investigating and applying more simplistic approaches. Interestingly, through a review of current literature and research efforts, it seems that the learning and teaching principles in digital medicine continue to push towards the development of sophisticated algorithms with a limited scope and has not fully embraced or encouraged a shift towards more simple solutions that yield equal or better results. This short note aims to demonstrate that within the world of digital medicine and engineering, simpler algorithms can offer effective and efficient solutions, where traditionally more complex algorithms have been used. Moreover, the note demonstrates that bridging different research disciplines is very beneficial and yields valuable insights and results.

Decomposing Time Series Data by a Non-negative Matrix Factorization Algorithm with Temporally Constrained Coefficients

PubMed Central

Cheung, Vincent C. K.; Devarajan, Karthik; Severini, Giacomo; Turolla, Andrea; Bonato, Paolo

2017-01-01

The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a less-than-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures. PMID:26737046

Measuring motivation using the transtheoretical (stages of change) model: A follow-up study of people who failed an online hearing screening.

PubMed

Ingo, Elisabeth; Brännström, K Jonas; Andersson, Gerhard; Lunner, Thomas; Laplante-Lévesque, Ariane

2016-07-01

Acceptance and readiness to seek professional help have shown to be important factors for favourable audiological rehabilitation outcomes. Theories from health psychology such as the transtheoretical (stages-of-change) model could help understand behavioural change in people with hearing impairment. In recent studies, the University of Rhode Island change assessment (URICA) has been found to have good predictive validity. In a previous study, 224 Swedish adults who had failed an online hearing screening completed URICA and two other measures of stages of change. This follow-up aimed to: (1) determine prevalence of help-seeking at a hearing clinic and hearing aid uptake, and (2) explore the predictive validity of the stages of change measures by a follow-up on the 224 participants who had failed a hearing screening 18 months previously. A total of 122 people (54%) completed the follow-up online questionnaire, including the three measures and questions regarding experience with hearing help-seeking and hearing aid uptake. Since failing the online hearing screening, 61% of participants had sought help. A good predictive validity for a one-item measure of stages of change was reported. The Staging algorithm was the stages of change measure with the best ability to predict help-seeking 18 months later.

Effects of help-seeking in a blended high school Biology class

NASA Astrophysics Data System (ADS)

Deguzman, Paolo

Distance learning provides an opportunity for students to learn valuable information through technology and interactive media. Distance learning additionally offers educational institutions the flexibility of synchronous and asynchronous instruction while increasing enrollment and lowering cost. However, distance education has not been well documented within the context of urban high schools. Distance learning may allow high school students to understand material at an individualized pace for either enrichment or remediation. A successful high school student who participates in distance learning should exhibit high self regulatory skills. However, most urban high school students have not been exposed to distance learning and should be introduced to proper self regulatory strategies that should increase the likelihood of understanding the material. To help facilitate a move into distance learning, a blended distance learning model, the combination of distance learning and traditional learning, will be used. According to O'Neil's (in preparation) revised problem solving model, self regulation is a component of problem solving. Within the Blended Biology course, urban high school students will be trained in help-seeking strategies to further their understanding of genetics and Punnett Square problem solving. This study investigated the effects of help-seeking in a blended high school Biology course. The main study consisted of a help-seeking group (n=55) and a control group (n=53). Both the help-seeking group and the control group were taught by one teacher for two weeks. The help-seeking group had access to Blended Biology with Help-Seeking while the control group only had access to Blended Biology. The main study used a pretest and posttest to measure Genetics Content Understanding, Punnett Square Problem Solving, Adaptive Help-Seeking, Maladaptive Help-Seeking, and Self Regulation. The analysis showed no significant difference in any of the measures in terms of help seeking. However, blended distance learning appeared to work as posttest means increased significantly from the pretest means. Future studies should consider the method of communication for help-seeking and help-giving within a high school distance learning context. Further studies should consider developing instruments to measure the difference in knowing when help is needed versus active choice.

Radio frequency interference mitigation using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

2017-01-01

We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

Glucose-responsive neurons of the paraventricular thalamus control sucrose-seeking behavior.

PubMed

Labouèbe, Gwenaël; Boutrel, Benjamin; Tarussio, David; Thorens, Bernard

2016-08-01

Feeding behavior is governed by homeostatic needs and motivational drive to obtain palatable foods. Here, we identify a population of glutamatergic neurons in the paraventricular thalamus of mice that express the glucose transporter Glut2 (encoded by Slc2a2) and project to the nucleus accumbens. These neurons are activated by hypoglycemia and, in freely moving mice, their activation by optogenetics or Slc2a2 inactivation increases motivated sucrose-seeking but not saccharin-seeking behavior. These neurons may control sugar overconsumption in obesity and diabetes.

[Research on magnetic coupling centrifugal blood pump control based on a self-tuning fuzzy PI algorithm].

PubMed

Yang, Lei; Yang, Ming; Xu, Zihao; Zhuang, Xiaoqi; Wang, Wei; Zhang, Haibo; Han, Lu; Xu, Liang

2014-10-01

The purpose of this paper is to report the research and design of control system of magnetic coupling centrifugal blood pump in our laboratory, and to briefly describe the structure of the magnetic coupling centrifugal blood pump and principles of the body circulation model. The performance of blood pump is not only related to materials and structure, but also depends on the control algorithm. We studied the algorithm about motor current double-loop control for brushless DC motor. In order to make the algorithm adjust parameter change in different situations, we used the self-tuning fuzzy PI control algorithm and gave the details about how to design fuzzy rules. We mainly used Matlab Simulink to simulate the motor control system to test the performance of algorithm, and briefly introduced how to implement these algorithms in hardware system. Finally, by building the platform and conducting experiments, we proved that self-tuning fuzzy PI control algorithm could greatly improve both dynamic and static performance of blood pump and make the motor speed and the blood pump flow stable and adjustable.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Novel bio-inspired smart control for hazard mitigation of civil structures

NASA Astrophysics Data System (ADS)

Kim, Yeesock; Kim, Changwon; Langari, Reza

2010-11-01

In this paper, a new bio-inspired controller is proposed for vibration mitigation of smart structures subjected to ground disturbances (i.e. earthquakes). The control system is developed through the integration of a brain emotional learning (BEL) algorithm with a proportional-integral-derivative (PID) controller and a semiactive inversion (Inv) algorithm. The BEL algorithm is based on the neurologically inspired computational model of the amygdala and the orbitofrontal cortex. To demonstrate the effectiveness of the proposed hybrid BEL-PID-Inv control algorithm, a seismically excited building structure equipped with a magnetorheological (MR) damper is investigated. The performance of the proposed hybrid BEL-PID-Inv control algorithm is compared with that of passive, PID, linear quadratic Gaussian (LQG), and BEL control systems. In the simulation, the robustness of the hybrid BEL-PID-Inv control algorithm in the presence of modeling uncertainties as well as external disturbances is investigated. It is shown that the proposed hybrid BEL-PID-Inv control algorithm is effective in improving the dynamic responses of seismically excited building structure-MR damper systems.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

NASA Astrophysics Data System (ADS)

Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

2015-08-01

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

Distributed Learning, Extremum Seeking, and Model-Free Optimization for the Resilient Coordination of Multi-Agent Adversarial Groups

DTIC Science & Technology

2016-09-07

been demonstrated on maximum power point tracking for photovoltaic arrays and for wind turbines . 3. ES has recently been implemented on the Mars...high-dimensional optimization problems . Extensions and applications of these techniques were developed during the realization of the project. 15...studied problems of dynamic average consensus and a class of unconstrained continuous-time optimization algorithms for the coordination of multiple

Sensor agnostic object recognition using a map seeking circuit

NASA Astrophysics Data System (ADS)

Overman, Timothy L.; Hart, Michael

2012-05-01

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

The Communication of Help-Seeking Skills to Children.

ERIC Educational Resources Information Center

Truitt, Judi C.; Kalbfleisch, Pamela J.

A study examined whether theatrical performances can communicate appropriate help-seeking skills to children for avoiding sexual abuse. The study compared the effectiveness of a play in which characters seek help for sexual abuse with that of a similar videotaped presentation, and with a control group that viewed neither the play nor the…

Cancer Fatalism, Literacy, and Cancer Information Seeking in the American Public

ERIC Educational Resources Information Center

Kobayashi, Lindsay C.; Smith, Samuel G.

2016-01-01

Information seeking is an important behavior for cancer prevention and control, but inequalities in the communication of information about the disease persist. Conceptual models have suggested that low health literacy is a barrier to information seeking, and that fatalistic beliefs about cancer may be a mediator of this relationship. Cancer…

The Effects of Using a Model-Reinforced Video on Information-Seeking Behaviour

ERIC Educational Resources Information Center

McHugh, Elizabeth A.; Lenz, Janet G.; Reardon, Robert C.; Peterson, Gary W.

2012-01-01

This study examined the effects of viewing a ten-minute model-reinforced video on careers information-seeking behaviour of 280 students in ten sections of a university careers course randomly assigned to treatment or control conditions. The video portrayed an undergraduate student seeking careers counselling services and a counsellor using…

The Association between Sensation Seeking and E-cigarette Use in Texas Young Adults: A Cross-Sectional Study

PubMed Central

Case, Kathleen; Loukas, Alexandra; Harrell, Melissa; Wilkinson, Anna; Springer, Andrew; Pérez, Adriana; Creamer, MeLisa; Perry, Cheryl L.

2017-01-01

Objective To examine the associations between sensation seeking and ever and current e-cigarette use in Texas young adults (18–29 years old). Current cigarette use was examined as a potential effect modifier of the associations. Participants Participants included college students enrolled in four-year and two-year colleges in four metropolitan areas in Texas (n=5,418) who completed the survey between November 2014 and February 2015. Methods This cross-sectional study utilized mixed effects logistic regression to determine the associations between mean sensation seeking scores and ever and current e-cigarette use after controlling for covariates. Results After controlling for covariates, significant associations between sensation seeking and both ever and current e-cigarette use were observed, however, these associations were significant for non-current smokers only (AOR=1.55, 95% CI=1.39, 1.73; AOR=1.82, 95% CI=1.54, 2.15, respectively). Conclusions Sensation seeking is an important factor in identifying college students who may be at increased risk for e-cigarette use behaviors. Keywords: Electronic cigarettes, sensation seeking, current cigarette use PMID:28095126

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Automatic control algorithm effects on energy production

NASA Technical Reports Server (NTRS)

Mcnerney, G. M.

1981-01-01

A computer model was developed using actual wind time series and turbine performance data to simulate the power produced by the Sandia 17-m VAWT operating in automatic control. The model was used to investigate the influence of starting algorithms on annual energy production. The results indicate that, depending on turbine and local wind characteristics, a bad choice of a control algorithm can significantly reduce overall energy production. The model can be used to select control algorithms and threshold parameters that maximize long term energy production. The results from local site and turbine characteristics were generalized to obtain general guidelines for control algorithm design.

NeuroSeek dual-color image processing infrared focal plane array

NASA Astrophysics Data System (ADS)

McCarley, Paul L.; Massie, Mark A.; Baxter, Christopher R.; Huynh, Buu L.

1998-09-01

Several technologies have been developed in recent years to advance the state of the art of IR sensor systems including dual color affordable focal planes, on-focal plane array biologically inspired image and signal processing techniques and spectral sensing techniques. Pacific Advanced Technology (PAT) and the Air Force Research Lab Munitions Directorate have developed a system which incorporates the best of these capabilities into a single device. The 'NeuroSeek' device integrates these technologies into an IR focal plane array (FPA) which combines multicolor Midwave IR/Longwave IR radiometric response with on-focal plane 'smart' neuromorphic analog image processing. The readout and processing integrated circuit very large scale integration chip which was developed under this effort will be hybridized to a dual color detector array to produce the NeuroSeek FPA, which will have the capability to fuse multiple pixel-based sensor inputs directly on the focal plane. Great advantages are afforded by application of massively parallel processing algorithms to image data in the analog domain; the high speed and low power consumption of this device mimic operations performed in the human retina.

Adaptive Control Strategies for Flexible Robotic Arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1996-01-01

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Effects of tick control by acaricide self-treatement of white-tailed deer on host-seeking tick infection prevalence and entomologic risk for Ixodes scapularis-borne pathogens

USDA-ARS?s Scientific Manuscript database

We evaluated the effects of tick control by acaricide self-treatment of white-tailed deer on the infection prevalence and entomologic risk for three I. scapularis-borne bacteria in host-seeking ticks. Ticks were collected from vegetation in areas treated with the ‘4-Poster’ device and from control a...

Can a documentary increase help-seeking intentions in men? A randomised controlled trial.

PubMed

King, Kylie Elizabeth; Schlichthorst, Marisa; Spittal, Matthew J; Phelps, Andrea; Pirkis, Jane

2018-01-01

We investigated whether a public health intervention-a three-part documentary called Man Up which explored the relationship between masculinity and mental health, well-being and suicidality-could increase men's intentions to seek help for personal and emotional problems. We recruited men aged 18 years or over who were not at risk of suicide to participate in a double-blind randomised controlled trial. Participants were randomly assigned (1:1) via computer randomisation to view Man Up (the intervention) or a control documentary. We hypothesised that 4 weeks after viewing Man Up participants would report higher levels of intention to seek help than those who viewed the control documentary. Our primary outcome was assessed using the General Help Seeking Questionnaire, and was analysed for all participants. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12616001169437, Universal Trial Number: U1111-1186-1459) and was funded by the Movember Foundation. Three hundred and fifty-four men were assessed for eligibility for the trial and randomised to view Man Up or the control documentary. Of these, 337 completed all stages (nine participants were lost to follow-up in the intervention group and eight in the control group). Linear regression analysis showed a significant increase in intentions to seek help in the intervention group, but not in the control group (coef.=2.06, 95% CI 0.48 to 3.63, P=0.01). Our trial demonstrates the potential for men's health outcomes to be positively impacted by novel, media-based public health interventions that focus on traditional masculinity. ACTRN12616001169437, Results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Can a documentary increase help-seeking intentions in men? A randomised controlled trial

PubMed Central

Schlichthorst, Marisa; Spittal, Matthew J; Phelps, Andrea; Pirkis, Jane

2018-01-01

Background We investigated whether a public health intervention—a three-part documentary called Man Up which explored the relationship between masculinity and mental health, well-being and suicidality—could increase men’s intentions to seek help for personal and emotional problems. Methods We recruited men aged 18 years or over who were not at risk of suicide to participate in a double-blind randomised controlled trial. Participants were randomly assigned (1:1) via computer randomisation to view Man Up (the intervention) or a control documentary. We hypothesised that 4 weeks after viewing Man Up participants would report higher levels of intention to seek help than those who viewed the control documentary. Our primary outcome was assessed using the General Help Seeking Questionnaire, and was analysed for all participants. The trial was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12616001169437, Universal Trial Number: U1111-1186-1459) and was funded by the Movember Foundation. Results Three hundred and fifty-four men were assessed for eligibility for the trial and randomised to view Man Up or the control documentary. Of these, 337 completed all stages (nine participants were lost to follow-up in the intervention group and eight in the control group). Linear regression analysis showed a significant increase in intentions to seek help in the intervention group, but not in the control group (coef.=2.06, 95% CI 0.48 to 3.63, P=0.01). Conclusions Our trial demonstrates the potential for men’s health outcomes to be positively impacted by novel, media-based public health interventions that focus on traditional masculinity. Trial registration number ACTRN12616001169437, Results. PMID:29101215

Is Sensation Seeking a correlate of excessive behaviors and behavioral addictions? A detailed examination of patients with Gambling Disorder and Internet Addiction.

PubMed

Müller, K W; Dreier, M; Beutel, M E; Wölfling, K

2016-08-30

Sensation Seeking has repeatedly been related to substance use. Also, its role as a correlate of Gambling Disorder has been discussed although research has led to heterogeneous results. Likewise, first studies on Internet Addiction have indicated increased Sensation Seeking, to some extent contradicting clinical impression of patients suffering from internet addiction. We assessed Sensation Seeking in a clinical sample of n=251 patients with Gambling Disorder, n=243 patients with internet addiction, n=103 clients with excessive but not addictive internet use, and n=142 healthy controls. The clinical groups were further sub-divided according to the preferred type of addictive behavior (slot-machine gambling vs. high arousal gambling activities and internet gaming disorder vs. other internet-related addictive behaviors). Decreased scores in some subscales of Sensation Seeking were found among male patients compared to healthy controls with no differences between patients with Gambling Disorder and Internet Addiction. The type of preferred gambling or online activity was not related to differences in Sensation Seeking. Previous findings indicating only small associations between Sensation Seeking and Gambling Disorder were confirmed. Regarding Internet Addiction our results contradict findings from non-clinical samples. Sensation Seeking might be relevant in initiating contact to the health care system. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

PubMed

Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

2017-08-01

Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research. Copyright © 2017. Published by Elsevier Inc.

Model reference adaptive control of robots

NASA Technical Reports Server (NTRS)

Steinvorth, Rodrigo

1991-01-01

This project presents the results of controlling two types of robots using new Command Generator Tracker (CGT) based Direct Model Reference Adaptive Control (MRAC) algorithms. Two mathematical models were used to represent a single-link, flexible joint arm and a Unimation PUMA 560 arm; and these were then controlled in simulation using different MRAC algorithms. Special attention was given to the performance of the algorithms in the presence of sudden changes in the robot load. Previously used CGT based MRAC algorithms had several problems. The original algorithm that was developed guaranteed asymptotic stability only for almost strictly positive real (ASPR) plants. This condition is very restrictive, since most systems do not satisfy this assumption. Further developments to the algorithm led to an expansion of the number of plants that could be controlled, however, a steady state error was introduced in the response. These problems led to the introduction of some modifications to the algorithms so that they would be able to control a wider class of plants and at the same time would asymptotically track the reference model. This project presents the development of two algorithms that achieve the desired results and simulates the control of the two robots mentioned before. The results of the simulations are satisfactory and show that the problems stated above have been corrected in the new algorithms. In addition, the responses obtained show that the adaptively controlled processes are resistant to sudden changes in the load.

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

Unal, Resit

1994-01-01

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Abbreviation definition identification based on automatic precision estimates.

PubMed

Sohn, Sunghwan; Comeau, Donald C; Kim, Won; Wilbur, W John

2008-09-25

The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation. On the Medstract corpus our algorithm produced 97% precision and 85% recall which is higher than previously reported results. We also annotated 1250 randomly selected MEDLINE records as a gold standard. On this set we achieved 96.5% precision and 83.2% recall. This compares favourably with the well known Schwartz and Hearst algorithm. We developed an algorithm for abbreviation identification that uses a variety of strategies to identify the most probable definition for an abbreviation and also produces an estimated accuracy of the result. This process is purely automatic.

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

PubMed Central

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

Toward Optimal Target Placement for Neural Prosthetic Devices

PubMed Central

Cunningham, John P.; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Shenoy, Krishna V.

2008-01-01

Neural prosthetic systems have been designed to estimate continuous reach trajectories (motor prostheses) and to predict discrete reach targets (communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses. PMID:18829845

Meta-heuristic algorithms as tools for hydrological science

NASA Astrophysics Data System (ADS)

Yoo, Do Guen; Kim, Joong Hoon

2014-12-01

In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

NASA Astrophysics Data System (ADS)

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-03-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

PubMed Central

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-01-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework. PMID:26987587

The combined control algorithm for large-angle maneuver of HITSAT-1 small satellite

NASA Astrophysics Data System (ADS)

Zhaowei, Sun; Yunhai, Geng; Guodong, Xu; Ping, He

2004-04-01

The HITSAT-1 is the first small satellite developed by Harbin Institute of Technology (HIT) whose mission objective is to test several pivotal techniques. The large angle maneuver control is one of the pivotal techniques of HITSAT-1 and the instantaneous Eulerian axis control algorithm (IEACA) has been applied. Because of using the reaction wheels and magnetorquer as the control actuators, the combined control algorithm has been adopted during the large-angle maneuver course. The computer simulation based on the MATRIX×6.0 software has finished and the results indicated that the combined control algorithm reduced the reaction wheel speeds obviously, and the IEACA algorithm has the advantages of simplicity and efficiency.

A tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel independent brake from moderate driving to limit handling

NASA Astrophysics Data System (ADS)

Joa, Eunhyek; Park, Kwanwoo; Koh, Youngil; Yi, Kyongsu; Kim, Kilsoo

2018-04-01

This paper presents a tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel braking for enhanced performance from moderate driving to limit handling. The proposed algorithm adopted hierarchical structure: supervisor - desired motion tracking controller - optimisation-based control allocation. In the supervisor, by considering transient cornering characteristics, desired vehicle motion is calculated. In the desired motion tracking controller, in order to track desired vehicle motion, virtual control input is determined in the manner of sliding mode control. In the control allocation, virtual control input is allocated to minimise cost function. The cost function consists of two major parts. First part is a slip-based tyre friction utilisation quantification, which does not need a tyre force estimation. Second part is an allocation guideline, which guides optimally allocated inputs to predefined solution. The proposed algorithm has been investigated via simulation from moderate driving to limit handling scenario. Compared to Base and direct yaw moment control system, the proposed algorithm can effectively reduce tyre dissipation energy in the moderate driving situation. Moreover, the proposed algorithm enhances limit handling performance compared to Base and direct yaw moment control system. In addition to comparison with Base and direct yaw moment control, comparison the proposed algorithm with the control algorithm based on the known tyre force information has been conducted. The results show that the performance of the proposed algorithm is similar with that of the control algorithm with the known tyre force information.

Development of a control algorithm for the ultrasound scanning robot (NCCUSR) using ultrasound image and force feedback.

PubMed

Kim, Yeoun Jae; Seo, Jong Hyun; Kim, Hong Rae; Kim, Kwang Gi

2017-06-01

Clinicians who frequently perform ultrasound scanning procedures often suffer from musculoskeletal disorders, arthritis, and myalgias. To minimize their occurrence and to assist clinicians, ultrasound scanning robots have been developed worldwide. Although, to date, there is still no commercially available ultrasound scanning robot, many control methods have been suggested and researched. These control algorithms are either image based or force based. If the ultrasound scanning robot control algorithm was a combination of the two algorithms, it could benefit from the advantage of each one. However, there are no existing control methods for ultrasound scanning robots that combine force control and image analysis. Therefore, in this work, a control algorithm is developed for an ultrasound scanning robot using force feedback and ultrasound image analysis. A manipulator-type ultrasound scanning robot named 'NCCUSR' is developed and a control algorithm for this robot is suggested and verified. First, conventional hybrid position-force control is implemented for the robot and the hybrid position-force control algorithm is combined with ultrasound image analysis to fully control the robot. The control method is verified using a thyroid phantom. It was found that the proposed algorithm can be applied to control the ultrasound scanning robot and experimental outcomes suggest that the images acquired using the proposed control method can yield a rating score that is equivalent to images acquired directly by the clinicians. The proposed control method can be applied to control the ultrasound scanning robot. However, more work must be completed to verify the proposed control method in order to become clinically feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

NASA Astrophysics Data System (ADS)

Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

2013-07-01

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Algorithms for output feedback, multiple-model, and decentralized control problems

NASA Technical Reports Server (NTRS)

Halyo, N.; Broussard, J. R.

1984-01-01

The optimal stochastic output feedback, multiple-model, and decentralized control problems with dynamic compensation are formulated and discussed. Algorithms for each problem are presented, and their relationship to a basic output feedback algorithm is discussed. An aircraft control design problem is posed as a combined decentralized, multiple-model, output feedback problem. A control design is obtained using the combined algorithm. An analysis of the design is presented.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

PubMed

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy

NASA Astrophysics Data System (ADS)

Penfold, Scott; Zalas, Rafał; Casiraghi, Margherita; Brooke, Mark; Censor, Yair; Schulte, Reinhard

2017-05-01

A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy treatment planning with dose-volume constraints included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ-algorithm of Byrne combined with the automatic relaxation method that uses cyclic projections. Detailed implementation instructions are furnished. Functionality of the algorithm was demonstrated through the creation of an intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and also for a realistic base-of-skull chordoma treatment site. Monte Carlo simulations of proton pencil beams of varying energy were conducted to obtain dose distributions for the 2D test case. A research release of the Pinnacle 3 proton treatment planning system was used to extract pencil beam doses for a clinical base-of-skull chordoma case. In both cases the beamlet doses were calculated to satisfy dose-volume constraints according to our new algorithm. Examination of the dose-volume histograms following inverse planning with our algorithm demonstrated that it performed as intended. The application of our proposed algorithm to dose-volume constraint inverse planning was successfully demonstrated. Comparison with optimized dose distributions from the research release of the Pinnacle 3 treatment planning system showed the algorithm could achieve equivalent or superior results.

Outpatient Mental Health Treatment Utilization and Military Career Impact in the United States Marine Corps.

PubMed

Ghahramanlou-Holloway, Marjan; LaCroix, Jessica M; Koss, Kari; Perera, Kanchana U; Rowan, Anderson; VanSickle, Marcus R; Novak, Laura A; Trieu, Theresa H

2018-04-23

Service members (SM) are at increased risk of psychiatric conditions, including suicide, yet research indicates SMs believe seeking mental health treatment may negatively impact their military careers, despite a paucity of research examining actual career impacts. This study examined the link between seeking outpatient mental health (MH) treatment and military career impacts within the United States Marine Corps. In Phase 1, a retrospective medical record review of outpatient MH treatment-seeking Marines ( N = 38) was conducted. In Phase 2, a sample of outpatient MH treatment-seeking Marines ( N = 40) was matched to a non-treatment-seeking sample of Marines ( N = 138) to compare career-progression. In Phase 1, there were no significant links between demographic, military, and clinical characteristics and referral source or receipt of career-affecting treatment recommendations. In Phase 2, MH treatment-seeking Marines in outpatient settings were more likely than matched controls to be separated from the military (95.0% versus 63.0%, p = 0.002), but no more likely to experience involuntary separation. MH treatment-seeking Marines were more likely to have documented legal action (45.0% versus 23.9%, p = 0.008) and had a shorter time of military service following the index MH encounter than matched controls ( p < 0.001). Clinical, anti-stigma, and suicide prevention policy implications are discussed.

PSO Algorithm for an Optimal Power Controller in a Microgrid

NASA Astrophysics Data System (ADS)

Al-Saedi, W.; Lachowicz, S.; Habibi, D.; Bass, O.

2017-07-01

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.

Comparison of Controller and Flight Deck Algorithm Performance During Interval Management with Dynamic Arrival Trees (STARS)

NASA Technical Reports Server (NTRS)

Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.

2012-01-01

Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

A new compound arithmetic crossover-based genetic algorithm for constrained optimisation in enterprise systems

NASA Astrophysics Data System (ADS)

Jin, Chenxia; Li, Fachao; Tsang, Eric C. C.; Bulysheva, Larissa; Kataev, Mikhail Yu

2017-01-01

In many real industrial applications, the integration of raw data with a methodology can support economically sound decision-making. Furthermore, most of these tasks involve complex optimisation problems. Seeking better solutions is critical. As an intelligent search optimisation algorithm, genetic algorithm (GA) is an important technique for complex system optimisation, but it has internal drawbacks such as low computation efficiency and prematurity. Improving the performance of GA is a vital topic in academic and applications research. In this paper, a new real-coded crossover operator, called compound arithmetic crossover operator (CAC), is proposed. CAC is used in conjunction with a uniform mutation operator to define a new genetic algorithm CAC10-GA. This GA is compared with an existing genetic algorithm (AC10-GA) that comprises an arithmetic crossover operator and a uniform mutation operator. To judge the performance of CAC10-GA, two kinds of analysis are performed. First the analysis of the convergence of CAC10-GA is performed by the Markov chain theory; second, a pair-wise comparison is carried out between CAC10-GA and AC10-GA through two test problems available in the global optimisation literature. The overall comparative study shows that the CAC performs quite well and the CAC10-GA defined outperforms the AC10-GA.

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

The Theory of Planned Behavior as It Predicts Potential Intention to Seek Mental Health Services for Depression among College Students

PubMed Central

Bohon, Lisa M.; Cotter, Kelly A.; Kravitz, Richard L.; Cello, Philip C.; Garcia, Erik Fernandez y

2016-01-01

Between 9.5% and 31.3% of College students suffer from depression1, 2. Universities need to understand the factors that relate to care-seeking behavior. Objective Across 3 studies, to relate attitude, social norms, and perceived behavioral control to intention to seek mental health services, and to investigate barriers to care-seeking. Participants University college students (N = 845, 64% female, 26% male, and 10% unspecified). Method New measures were created in studies 1 and 2, and were examined using structural equation modeling in study 3. Results Partially consistent with the Theory of Planned Behavior3, a model with an excellent fit revealed that more positive attitudes about care and higher perceived behavioral control directly predicted higher intention to seek mental health services. Conclusions Educating college students about mental health disorders and treatments, enhancing knowledge about available services, and addressing limited access to long-term care might improve treatment rates for students suffering from depression. PMID:27386898

Strategic Control Algorithm Development : Volume 3. Strategic Algorithm Report.

DOT National Transportation Integrated Search

1974-08-01

The strategic algorithm report presents a detailed description of the functional basic strategic control arrival algorithm. This description is independent of a particular computer or language. Contained in this discussion are the geometrical and env...

Neural Generalized Predictive Control: A Newton-Raphson Implementation

NASA Technical Reports Server (NTRS)

Soloway, Donald; Haley, Pamela J.

1997-01-01

An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

Radiation induced fracture of the scapula

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riggs, J.H. III; Schultz, G.D.; Hanes, S.A.

A case of radiation induced osteonecrosis resulting in a fracture of the scapula in a 76-yr-old female patient with a history of breast carcinoma is presented. Diagnostic imaging, laboratory recommendations and clinical findings are discussed along with an algorithm for the safe management of patients with a history of cancer and musculoskeletal complaints. This case demonstrates the necessity of a thorough investigation of musculoskeletal complaints in patients with previous bone-seeking carcinomas.

Algorithms and Object-Oriented Software for Distributed Physics-Based Modeling

NASA Technical Reports Server (NTRS)

Kenton, Marc A.

2001-01-01

The project seeks to develop methods to more efficiently simulate aerospace vehicles. The goals are to reduce model development time, increase accuracy (e.g.,by allowing the integration of multidisciplinary models), facilitate collaboration by geographically- distributed groups of engineers, support uncertainty analysis and optimization, reduce hardware costs, and increase execution speeds. These problems are the subject of considerable contemporary research (e.g., Biedron et al. 1999; Heath and Dick, 2000).

Battlespace Awareness: Heterogeneous Sensor Maps of Large Scale, Complex Environments

DTIC Science & Technology

2017-06-13

reference frames enable a system designer to describe the position of any sensor or platform at any point of time. This section introduces the...analysis to evaluate the quality of reconstructions created by our algorithms. CloudCompare is an open-source tool designed for this purpose [65]. In...structure of the data. The data term seeks to keep the proposed solution (u) similar to the originally observed values ( f ). A systems designer must

Optimal Dynamic Strategies for Index Tracking and Algorithmic Trading

NASA Astrophysics Data System (ADS)

Ward, Brian

In this thesis we study dynamic strategies for index tracking and algorithmic trading. Tracking problems have become ever more important in Financial Engineering as investors seek to precisely control their portfolio risks and exposures over different time horizons. This thesis analyzes various tracking problems and elucidates the tracking errors and strategies one can employ to minimize those errors and maximize profit. In Chapters 2 and 3, we study the empirical tracking properties of exchange traded funds (ETFs), leveraged ETFs (LETFs), and futures products related to spot gold and the Chicago Board Option Exchange (CBOE) Volatility Index (VIX), respectively. These two markets provide interesting and differing examples for understanding index tracking. We find that static strategies work well in the nonleveraged case for gold, but fail to track well in the corresponding leveraged case. For VIX, tracking via neither ETFs, nor futures\\ portfolios succeeds, even in the nonleveraged case. This motivates the need for dynamic strategies, some of which we construct in these two chapters and further expand on in Chapter 4. There, we analyze a framework for index tracking and risk exposure control through financial derivatives. We derive a tracking condition that restricts our exposure choices and also define a slippage process that characterizes the deviations from the index over longer horizons. The framework is applied to a number of models, for example, Black Scholes model and Heston model for equity index tracking, as well as the Square Root (SQR) model and the Concatenated Square Root (CSQR) model for VIX tracking. By specifying how each of these models fall into our framework, we are able to understand the tracking errors in each of these models. Finally, Chapter 5 analyzes a tracking problem of a different kind that arises in algorithmic trading: schedule following for optimal execution. We formulate and solve a stochastic control problem to obtain the optimal trading rates using both market and limit orders. There is a quadratic terminal penalty to ensure complete liquidation as well as a trade speed limiter and trader director to provide better control on the trading rates. The latter two penalties allow the trader to tailor the magnitude and sign (respectively) of the optimal trading rates. We demonstrate the applicability of the model to following a benchmark schedule. In addition, we identify conditions on the model parameters to ensure optimality of the controls and finiteness of the associated value functions. Throughout the chapter, numerical simulations are provided to demonstrate the properties of the optimal trading rates.

Massively parallel algorithms for real-time wavefront control of a dense adaptive optics system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fijany, A.; Milman, M.; Redding, D.

1994-12-31

In this paper massively parallel algorithms and architectures for real-time wavefront control of a dense adaptive optic system (SELENE) are presented. The authors have already shown that the computation of a near optimal control algorithm for SELENE can be reduced to the solution of a discrete Poisson equation on a regular domain. Although, this represents an optimal computation, due the large size of the system and the high sampling rate requirement, the implementation of this control algorithm poses a computationally challenging problem since it demands a sustained computational throughput of the order of 10 GFlops. They develop a novel algorithm,more » designated as Fast Invariant Imbedding algorithm, which offers a massive degree of parallelism with simple communication and synchronization requirements. Due to these features, this algorithm is significantly more efficient than other Fast Poisson Solvers for implementation on massively parallel architectures. The authors also discuss two massively parallel, algorithmically specialized, architectures for low-cost and optimal implementation of the Fast Invariant Imbedding algorithm.« less

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Photovoltaic Cells Mppt Algorithm and Design of Controller Monitoring System

NASA Astrophysics Data System (ADS)

Meng, X. Z.; Feng, H. B.

2017-10-01

This paper combined the advantages of each maximum power point tracking (MPPT) algorithm, put forward a kind of algorithm with higher speed and higher precision, based on this algorithm designed a maximum power point tracking controller with ARM. The controller, communication technology and PC software formed a control system. Results of the simulation and experiment showed that the process of maximum power tracking was effective, and the system was stable.

Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed

NASA Technical Reports Server (NTRS)

Tian, Ye; Song, Qi; Cattafesta, Louis

2005-01-01

This report summarizes the activities on "Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed." The work summarized consists primarily of two parts. The first part summarizes our previous work and the extensions to adaptive ID and control algorithms. The second part concentrates on the validation of adaptive algorithms by applying them to a vibration beam test bed. Extensions to flow control problems are discussed.

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

Emergency ultrasound-based algorithms for diagnosing blunt abdominal trauma.

PubMed

Stengel, Dirk; Bauwens, Kai; Rademacher, Grit; Ekkernkamp, Axel; Güthoff, Claas

2013-07-31

Ultrasonography is regarded as the tool of choice for early diagnostic investigations in patients with suspected blunt abdominal trauma. Although its sensitivity is too low for definite exclusion of abdominal organ injury, proponents of ultrasound argue that ultrasound-based clinical pathways enhance the speed of primary trauma assessment, reduce the number of computed tomography scans and cut costs. To assess the effects of trauma algorithms that include ultrasound examinations in patients with suspected blunt abdominal trauma. We searched the Cochrane Injuries Group's Specialised Register, CENTRAL (The Cochrane Library), MEDLINE (OvidSP), EMBASE (OvidSP), CINAHL (EBSCO), publishers' databases, controlled trials registers and the Internet. Bibliographies of identified articles and conference abstracts were searched for further elligible studies. Trial authors were contacted for further information and individual patient data. The searches were updated in February 2013. randomised controlled trials (RCTs) and quasi-randomised trials (qRCTs). patients with blunt torso, abdominal or multiple trauma undergoing diagnostic investigations for abdominal organ injury. diagnostic algorithms comprising emergency ultrasonography (US). diagnostic algorithms without ultrasound examinations (for example, primary computed tomography [CT] or diagnostic peritoneal lavage [DPL]). mortality, use of CT and DPL, cost-effectiveness, laparotomy and negative laparotomy rates, delayed diagnoses, and quality of life. Two authors independently selected trials for inclusion, assessed methodological quality and extracted data. Where possible, data were pooled and relative risks (RRs), risk differences (RDs) and weighted mean differences, each with 95% confidence intervals (CIs), were calculated by fixed- or random-effects modelling, as appropriate. We identified four studies meeting our inclusion criteria. Overall, trials were of moderate methodological quality. Few trial authors responded to our written inquiries seeking to resolve controversial issues and to obtain individual patient data. We pooled mortality data from three trials involving 1254 patients; relative risk in favour of the US arm was 1.00 (95% CI 0.50 to 2.00). US-based pathways significantly reduced the number of CT scans (random-effects RD -0.52, 95% CI -0.83 to -0.21), but the meaning of this result is unclear. Given the low sensitivity of ultrasound, the reduction in CT scans may either translate to a number needed to treat or number needed to harm of two. There is currently insufficient evidence from RCTs to justify promotion of ultrasound-based clinical pathways in diagnosing patients with suspected blunt abdominal trauma.

A Fuel-Efficient Conflict Resolution Maneuver for Separation Assurance

NASA Technical Reports Server (NTRS)

Bowe, Aisha Ruth; Santiago, Confesor

2012-01-01

Automated separation assurance algorithms are envisioned to play an integral role in accommodating the forecasted increase in demand of the National Airspace System. Developing a robust, reliable, air traffic management system involves safely increasing efficiency and throughput while considering the potential impact on users. This experiment seeks to evaluate the benefit of augmenting a conflict detection and resolution algorithm to consider a fuel efficient, Zero-Delay Direct-To maneuver, when resolving a given conflict based on either minimum fuel burn or minimum delay. A total of twelve conditions were tested in a fast-time simulation conducted in three airspace regions with mixed aircraft types and light weather. Results show that inclusion of this maneuver has no appreciable effect on the ability of the algorithm to safely detect and resolve conflicts. The results further suggest that enabling the Zero-Delay Direct-To maneuver significantly increases the cumulative fuel burn savings when choosing resolution based on minimum fuel burn while marginally increasing the average delay per resolution.

The research of automatic speed control algorithm based on Green CBTC

NASA Astrophysics Data System (ADS)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

Computer Simulation of Electron Positron Annihilation Processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, y

2003-10-02

With the launching of the Next Linear Collider coming closer and closer, there is a pressing need for physicists to develop a fully-integrated computer simulation of e{sup +}e{sup -} annihilation process at center-of-mass energy of 1TeV. A simulation program acts as the template for future experiments. Either new physics will be discovered, or current theoretical uncertainties will shrink due to more accurate higher-order radiative correction calculations. The existence of an efficient and accurate simulation will help us understand the new data and validate (or veto) some of the theoretical models developed to explain new physics. It should handle well interfacesmore » between different sectors of physics, e.g., interactions happening at parton levels well above the QCD scale which are described by perturbative QCD, and interactions happening at much lower energy scale, which combine partons into hadrons. Also it should achieve competitive speed in real time when the complexity of the simulation increases. This thesis contributes some tools that will be useful for the development of such simulation programs. We begin our study by the development of a new Monte Carlo algorithm intended to perform efficiently in selecting weight-1 events when multiple parameter dimensions are strongly correlated. The algorithm first seeks to model the peaks of the distribution by features, adapting these features to the function using the EM algorithm. The representation of the distribution provided by these features is then improved using the VEGAS algorithm for the Monte Carlo integration. The two strategies mesh neatly into an effective multi-channel adaptive representation. We then present a new algorithm for the simulation of parton shower processes in high energy QCD. We want to find an algorithm which is free of negative weights, produces its output as a set of exclusive events, and whose total rate exactly matches the full Feynman amplitude calculation. Our strategy is to create the whole QCD shower as a tree structure generated by a multiple Poisson process. Working with the whole shower allows us to include correlations between gluon emissions from different sources. QCD destructive interference is controlled by the implementation of ''angular-ordering,'' as in the HERWIG Monte Carlo program. We discuss methods for systematic improvement of the approach to include higher order QCD effects.« less

Reassuringly calm? Self-reported patterns of responses to reassurance seeking in obsessive compulsive disorder.

PubMed

Salkovskis, Paul M; Kobori, Osamu

2015-12-01

The perception of threat and associated feelings of anxiety typically prompt people to seek safety; reassurance seeking is an interpersonal strategy almost universally used to reduce the immediate perception of risk. Excessive Reassurance Seeking (ERS) is considered to be particularly prominent and unequivocally counter-productive in people suffering from anxiety disorders in general and OCD in particular, producing short term relief but a longer term return and worsening of the original anxiety. We evaluated the extent and specificity of the effects of ERS in OCD and mechanisms involved in both anxiety relief and the hypothesized later return of anxiety.. Self rated effects of reassurance seeking were investigated in 153 individuals with OCD, 50 with panic disorder, and 52 healthy controls, evaluating reactions to the provision and non-provision of reassurance. Reassurance is associated with short term relief then longer term return of both discomfort and the urge to seek further reassurance in both anxious groups; healthy controls do not experience significant resurgence. Greater return of anxiety and urge to seek more reassurance were associated with higher levels of overall reassurance seeking.. The findings were based on retrospective self-report of naturally occurring episodes of ERS; prospective studies and induced behaviours are now needed. Not only is reassurance a quick fix for people experiencing OCD, but in the absence of treatment the only fix! The findings explain why reassurance seeking continues despite advice that it will worsen anxiety problems. Such advice is potentially harmful to patients and their loved ones.. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sensation seeking as a common factor in opioid dependent subjects and high risk sport practicing subjects. A cross sectional study.

PubMed

Franques, P; Auriacombe, M; Piquemal, E; Verger, M; Brisseau-Gimenez, S; Grabot, D; Tignol, J

2003-03-01

Animal research has outlined a vulnerability trait to drug dependence like behavior. The behavioral characteristic of this vulnerability is hyperactivity in response to a novel environment of which sensation seeking (SS) has been suggested as a possible equivalent in humans. If this is the case, SS should be more frequent in drug dependent and risky sports practicing subjects then controls. The objective of this study was to determine if opioid dependent subjects (ODS) and regular paragliders (RP) would be more SS then normal controls. Cross sectional study. Three groups of 34 individuals (total 102) matched for age and sex were selected from ODS seeking treatment, a paragliding club, and a college staff. Global and sub-scores of the Zuckerman sensation seeking scale (SSS). Non parametric statistics (Kruskal Wallis and Wilcoxon 2-Sample Tests) were used given the non-normal distribution of SSS scores in the ODS and RP groups. Significant differences were found across the three groups for the Thrill and Adventure Seeking (TAS) (P = 0.001), dishinibition (Dis) (P = 0.0003) and total score (P = 0.001). ODS and RP scored significantly higher than controls on two (Dis and the TAS scales). RP also scored significantly higher on the Boredom Susceptibility (BS) scale (P = 0.04). Our results show that RP and ODS differ from controls and have some similarities based on the SSS. In this study, the ODS and the RP could express different forms of a general tendency to seek intense and abrupt sensations through various behaviors. Our results in humans are in favor of the hypothesis that the behavioral trait of vulnerability to drug dependence behavior is expressed through SS. Copyright 2002 Elsevier Science Ireland Ltd.

Dehydroepiandrosterone Attenuates Cocaine-Seeking Behaviour Independently of Corticosterone Fluctuations.

PubMed

Maayan, R; Hirsh, L; Yadid, G; Weizman, A

2015-11-01

The neurosteroid dehydroepiandrosterone (DHEA) is involved in the pathophysiology of several psychiatric disorders, including cocaine addiction. We have previously shown that DHEA attenuates cocaine-seeking behaviour, and also that DHEA decreases corticosterone (CORT) levels in plasma and the prefrontal cortex. Previous studies have found that rats demonstrate cocaine-seeking behaviour only when the level of CORT reaches a minimum threshold. In the present study, we investigated whether the attenuating effect of DHEA on cocaine seeking is a result of it reducing CORT levels rather than a result of any unique neurosteroid properties. Rats received either daily DHEA injections (2 mg/kg, i.p.) alone, daily DHEA (2 mg/kg, i.p.) with CORT infusion (to maintain stable basal levels of CORT; 15 mg/kg, s.c.) or vehicle (i.p.) as control, throughout self-administration training and extinction sessions. We found that both DHEA-treated and DHEA + CORT-treated groups showed a significantly lower number of active lever presses compared to controls throughout training and extinction sessions, as well as at cocaine-primed reinstatement. DHEA-treated rats showed lower CORT levels throughout the experimental phases compared to DHEA + CORT-treated and control rats. Additionally, we show that DHEA administered to cocaine-trained rats throughout extinction sessions, or immediately before reinstatement, attenuated cocaine seeking. These findings indicate that DHEA attenuates cocaine-seeking behaviour independently of fluctuations in CORT levels. © 2015 British Society for Neuroendocrinology.

Depression knowledge in high school students: effectiveness of the adolescent depression awareness program.

PubMed

Ruble, Anne E; Leon, Phillip J; Gilley-Hensley, Laura; Hess, Sally G; Swartz, Karen L

2013-09-25

Major depression is a common disorder among teenagers and is associated with significant morbidity and mortality. Suicide is the third leading cause of death among 15-24 year olds. Early identification and treatment is essential to prevent suicide. Depression education is a potential intervention for improving knowledge about depression and help-seeking behavior. The Adolescent Depression Awareness Program (ADAP) is a school-based depression education intervention with a core message that depression is a treatable medical illness. 710 high school students from six schools in Tulsa, OK participated in the study comparing changes in knowledge about depression and attitudes toward treatment-seeking between students receiving the intervention and those who did not. Changes in depression knowledge and attitude toward help-seeking were measured using the ADAP Depression Knowledge Questionnaire (ADKQ). There was a significant positive change in ADKQ score for students receiving the intervention but not in the control group. The intervention group also demonstrated a significant difference in willingness to "tell someone" if concerned about depression in a peer, which was not present in the control group. The students were not randomized to the intervention and control groups. The ADKQ evaluates attitudes about help-seeking but not behavior. A school-based educational intervention improved knowledge about depression and attitudes toward help-seeking in adolescents. Future studies should investigate if such change in knowledge results in help-seeking behaviors. © 2013 Elsevier B.V. All rights reserved.

Masculinity and barriers to seeking counseling: The buffering role of self-compassion.

PubMed

Heath, Patrick J; Brenner, Rachel E; Vogel, David L; Lannin, Daniel G; Strass, Haley A

2017-01-01

Less than 1/3 of college men seek psychological help per year when experiencing mental health concerns. Many believe this is because socialized masculine norms are incongruent with help-seeking decisions. In line with this, adherence to masculine norms, like emotional control and self-reliance, is consistently linked to factors associated with lower use of counseling. Identifying constructs that buffer, or reduce, the relationship between masculine norm adherence and common barriers to seeking help, like help-seeking self-stigma and resistance to self-disclosing, could shed light on mechanisms through which effective interventions could be developed. As such, this study examined whether self-compassion, or the ability to show oneself kindness and understanding in the face of challenges, moderated the relationship between masculine norm adherence and both help-seeking self-stigma and the risks associated with self-disclosing to a counselor in a sample of 284 undergraduate men (Mage = 19.68, range = 18-30). Results indicate that self-compassion is associated with lower levels of help-seeking self-stigma and disclosure risks. Additionally, and perhaps more importantly, self-compassion buffered the relationship between overall masculine norm adherence and each of these barriers. Furthermore, when specific masculine norms were examined, self-compassion buffered the relationship between emotional control and disclosure risks. These results support the need for future research focused on the development and assessment of self-compassion based interventions aimed at decreasing the barriers undergraduate men experience toward seeking psychological help. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Midbrain dopamine receptor availability is inversely associated with novelty-seeking traits in humans.

PubMed

Zald, David H; Cowan, Ronald L; Riccardi, Patrizia; Baldwin, Ronald M; Ansari, M Sib; Li, Rui; Shelby, Evan S; Smith, Clarence E; McHugo, Maureen; Kessler, Robert M

2008-12-31

Novelty-seeking personality traits are a major risk factor for the development of drug abuse and other unsafe behaviors. Rodent models of temperament indicate that high novelty responding is associated with decreased inhibitory autoreceptor control of midbrain dopamine neurons. It has been speculated that individual differences in dopamine functioning also underlie the personality trait of novelty seeking in humans. However, differences in the dopamine system of rodents and humans, as well as the methods for assessing novelty responding/seeking across species leave unclear to what extent the animal models inform our understanding of human personality. In the present study we examined the correlation between novelty-seeking traits in humans and D(2)-like (D(2)/D(3)) receptor availability in the substantia nigra/ventral tegmental area. Based on the rodent literature we predicted that novelty seeking would be characterized by lowered levels of D(2)-like (auto)receptor availability in the midbrain. Thirty-four healthy adults (18 men, 16 women) completed the Tridimensional Personality Questionnaire-Novelty-Seeking Scale and PET scanning with the D(2)/D(3) ligand [(18)F]fallypride. Novelty-Seeking personality traits were inversely associated with D(2)-like receptor availability in the ventral midbrain, an effect that remained significant after controlling for age. We speculate that the lower midbrain (auto)receptor availability seen in high novelty seekers leads to accentuated dopaminergic responses to novelty and other conditions that induce dopamine release.

Midbrain dopamine receptor availability is inversely associated with novelty seeking traits in humans

PubMed Central

Zald, David H.; Cowan, Ronald L.; Riccardi, Patrizia; Baldwin, Ronald M.; Ansari, M. Sib; Li, Rui; Shelby, Evan S.; Smith, Clarence E.; McHugo, Maureen; Kessler, Robert M.

2009-01-01

Novelty seeking personality traits are a major risk factor for the development of drug abuse and other unsafe behaviors. Rodent models of temperament indicate that high novelty responding is associated with decreased inhibitory autoreceptor control of midbrain dopamine neurons. It has been speculated that individual differences in dopamine functioning also underlie the personality trait of novelty seeking in humans. However, differences in the dopamine system of rodents and humans, as well as the methods for assessing novelty responding/seeking across species leave unclear to what extent the animal models inform our understanding of human personality. In the present study we examined the correlation between novelty seeking traits in humans and D2-like (D2/D3) receptor availability in the substantia nigra/ventral tegmental area. Based on the rodent literature we predicted that novelty seeking would be characterized by lowered levels of D2-like (auto)receptor availability in the midbrain. 34 healthy adults (18 men, 16 women) completed the Tridimensional Personality Questionnaire-Novelty Seeking Scale and PET scanning with the D2/D3 ligand [18F]fallypride. Novelty seeking personality traits were inversely associated with D2-like receptor availability in the ventral midbrain, an effect that remained significant after controlling for age. We speculate that the lower midbrain (auto)receptor availability seen in high novelty seekers leads to accentuated dopaminergic responses to novelty and other conditions that induce DA release. PMID:19118170

Relationship between substance use and attitudes towards seeking professional psychological help among veterans filing PTSD claims.

PubMed

Meshberg-Cohen, Sarah; Kachadourian, Lorig; Black, Anne C; Rosen, Marc I

2017-11-01

Veterans in distress often do not seek mental health treatment, even when such services are available. Substance use may further undermine treatment-seeking, given its association with negative treatment views. This study examined attitudes towards seeking psychological help in a sample of veterans diagnosed with posttraumatic stress disorder (PTSD), with and without co-occurring substance use disorders (SUD). Altogether, 143 male OEF/OIF veterans filing service-connected benefits claims for PTSD completed the Attitudes Towards Seeking Professional Psychological Help-Short Form (ATSPPH-SF) and other baseline assessments. Treatment attitudes were compared among veterans with (n=34) and without (n=109) SUD using ANCOVA, controlling for demographic covariates. Post-hoc ANCOVA compared means on the two ATSPPH-SF subscales: Openness to Seeking Treatment, and Value/Need in Seeking Treatment. Overall, ATSPPH-SF scores were similar to those reported in other samples of young men. Controlling for demographic covariates, veterans with co-occurring SUD held significantly less favorable attitudes towards seeking help than veterans without comorbid SUD. In subscale analyses, valuation of treatment was significantly lower among veterans with SUDs, but openness towards treatment was not. Substance-using veterans' lower valuation of treatment may reflect opinions that problems resolve on their own, psychotherapy is ineffective, or concerns that SUDs complicate treatment. Thus an approach towards engaging these veterans in treatment that addresses a general skepticism towards the value of psychological help is warranted. Published by Elsevier Ltd.

Low control over palatable food intake in rats is associated with habitual behavior and relapse vulnerability: individual differences.

PubMed

de Jong, Johannes W; Meijboom, Karin E; Vanderschuren, Louk J M J; Adan, Roger A H

2013-01-01

The worldwide obesity epidemic poses an enormous and growing threat to public health. However, the neurobehavioral mechanisms of overeating and obesity are incompletely understood. It has been proposed that addiction-like processes may underlie certain forms of obesity, in particular those associated with binge eating disorder. To investigate the role of addiction-like processes in obesity, we adapted a model of cocaine addiction-like behavior in rats responding for highly palatable food. Here, we tested whether rats responding for highly palatable chocolate Ensure would come to show three criteria of addiction-like behavior, i.e., high motivation, continued seeking despite signaled non-availability and persistence of seeking despite aversive consequences. We also investigated whether exposure to a binge model (a diet consisting of alternating periods of limited food access and access to highly palatable food), promotes the appearance of food addiction-like behavior. Our data show substantial individual differences in control over palatable food seeking and taking, but no distinct subgroup of animals showing addiction-like behavior could be identified. Instead, we observed a wide range extending from low to very high control over palatable food intake. Exposure to the binge model did not affect control over palatable food seeking and taking, however. Animals that showed low control over palatable food intake (i.e., scored high on the three criteria for addiction-like behavior) were less sensitive to devaluation of the food reward and more prone to food-induced reinstatement of extinguished responding, indicating that control over palatable food intake is associated with habitual food intake and vulnerability to relapse. In conclusion, we present an animal model to assess control over food seeking and taking. Since diminished control over food intake is a major factor in the development of obesity, understanding its behavioral and neural underpinnings may facilitate improved management of the obesity epidemic.

Learning control system design based on 2-D theory - An application to parallel link manipulator

NASA Technical Reports Server (NTRS)

Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

1990-01-01

An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

Chronic EtOH effects on putative measures of compulsive behavior in mice

PubMed Central

Radke, Anna K.; Jury, Nicholas J.; Kocharian, Adrina; Marcinkiewcz, Catherine A.; Lowery-Gionta, Emily G.; Pleil, Kristen E.; McElligott, Zoe A.; McKlveen, Jessica M.; Kash, Thomas L.; Holmes, Andrew

2015-01-01

Addictions, including alcohol use disorders (AUDs), are characterized by the loss of control over drug seeking and consumption, but the neural circuits and signaling mechanisms responsible for the transition from controlled use to uncontrolled abuse remain incompletely understood. Prior studies have shown that ‘compulsive-like’ behaviors in rodents, e.g., persistent responding for ethanol (EtOH) despite punishment, are increased after chronic exposure to EtOH. The main goal of the current study was to assess the effects of chronic intermittent EtOH (CIE) exposure on multiple, putative measures of compulsive-like EtOH seeking in C57BL/6J mice. Mice were exposed to two or four weekly cycles of CIE and then, post-withdrawal, tested for progressive ratio responding for EtOH, sustained responding during signaled EtOH-unavailability and (footshock) punished-suppression of responding for EtOH. Results showed that mice exposed to CIE exhibited attenuated suppression of EtOH-seeking during punishment, as compared to air-exposed controls. By contrast, CIE exposure affected neither punished food reward-seeking behavior, nor other putative measures of compulsive-like EtOH-seeking. Ex vivo RT-PCR analysis of brain tissue found reduced sensitivity to punished EtOH-seeking after CIE exposure was accompanied by a significant increase in gene expression of the GluN1 and GluN2A subunits of the N-methyl-D-aspartate receptor (NMDAR), specifically in the medial orbitofrontal cortex (mOFC). Moreover, slice electrophysiological analysis revealed increased NMDAR-mediated currents in the mOFC after CIE exposure in test-naïve mice. Collectively, the current findings add to growing body of evidence demonstrating that chronic exposure to EtOH fosters resistance to punished EtOH-seeking in association with adaptations in cortical glutamatergic transmission. PMID:26687341

New multirate sampled-data control law structure and synthesis algorithm

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.; Yang, Gen-Sheng

1992-01-01

A new multirate sampled-data control law structure is defined and a new parameter-optimization-based synthesis algorithm for that structure is introduced. The synthesis algorithm can be applied to multirate, multiple-input/multiple-output, sampled-data control laws having a prescribed dynamic order and structure, and a priori specified sampling/update rates for all sensors, processor states, and control inputs. The synthesis algorithm is applied to design two-input, two-output tip position controllers of various dynamic orders for a sixth-order, two-link robot arm model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheinker, Alexander

Here, we study control of the angular-velocity actuated nonholonomic unicycle, via a simple, bounded extremum seeking controller which is robust to external disturbances and measurement noise. The vehicle performs source seeking despite not having any position information about itself or the source, able only to sense a noise corrupted scalar value whose extremum coincides with the unknown source location. In order to control the angular velocity, rather than the angular heading directly, a controller is developed such that the closed loop system exhibits multiple time scales and requires an analysis approach expanding the previous work of Kurzweil, Jarnik, Sussmann, andmore » Liu, utilizing weak limits. We provide analytic proof of stability and demonstrate how this simple scheme can be extended to include position-independent source seeking, tracking, and collision avoidance of groups on autonomous vehicles in GPS-denied environments, based only on a measure of distance to an obstacle, which is an especially important feature for an autonomous agent.« less

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

A Lyapunov based approach to energy maximization in renewable energy technologies

NASA Astrophysics Data System (ADS)

Iyasere, Erhun

This dissertation describes the design and implementation of Lyapunov-based control strategies for the maximization of the power captured by renewable energy harnessing technologies such as (i) a variable speed, variable pitch wind turbine, (ii) a variable speed wind turbine coupled to a doubly fed induction generator, and (iii) a solar power generating system charging a constant voltage battery. First, a torque control strategy is presented to maximize wind energy captured in variable speed, variable pitch wind turbines at low to medium wind speeds. The proposed strategy applies control torque to the wind turbine pitch and rotor subsystems to simultaneously control the blade pitch and tip speed ratio, via the rotor angular speed, to an optimum point at which the capture efficiency is maximum. The control method allows for aerodynamic rotor power maximization without exact knowledge of the wind turbine model. A series of numerical results show that the wind turbine can be controlled to achieve maximum energy capture. Next, a control strategy is proposed to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximal for a particular blade pitch angle and wind speed by using the generator rotor voltage as a control input. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge. Representative numerical results demonstrate that the wind turbine can be controlled to achieve near maximum energy capture. Finally, a power system consisting of a photovoltaic (PV) array panel, dc-to-dc switching converter, charging a battery is considered wherein the environmental conditions are time-varying. A backstepping PWM controller is developed to maximize the power of the solar generating system. The controller tracks a desired array voltage, designed online using an incremental conductance extremum-seeking algorithm, by varying the duty cycle of the switching converter. The stability of the control algorithm is demonstrated by means of Lyapunov analysis. Representative numerical results demonstrate that the grid power system can be controlled to track the maximum power point of the photovoltaic array panel in varying atmospheric conditions. Additionally, the performance of the proposed strategy is compared to the typical maximum power point tracking (MPPT) method of perturb and observe (P&O), where the converter dynamics are ignored, and is shown to yield better results.

Motor Control of Two Flywheels Enabling Combined Attitude Control and Bus Regulation

NASA Technical Reports Server (NTRS)

Kenny, Barbara H.

2004-01-01

This presentation discussed the flywheel technology development work that is ongoing at NASA GRC with a particular emphasis on the flywheel system control. The "field orientation" motor/generator control algorithm was discussed and explained. The position-sensorless angle and speed estimation algorithm was presented. The motor current response to a step change in command at low (10 kRPM) and high (60 kRPM) was discussed. The flywheel DC bus regulation control was explained and experimental results presented. Finally, the combined attitude control and energy storage algorithm that controls two flywheels simultaneously was presented. Experimental results were shown that verified the operational capability of the algorithm. shows high speed flywheel energy storage (60,000 RPM) and the successful implementation of an algorithm to simultaneously control both energy storage and a single axis of attitude with two flywheels. Overall, the presentation demonstrated that GRC has an operational facility that

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees.

PubMed

Kordi, Misagh; Bansal, Mukul S

2017-01-01

Duplication-Transfer-Loss (DTL) reconciliation has emerged as a powerful technique for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation takes as input a gene family phylogeny and the corresponding species phylogeny, and reconciles the two by postulating speciation, gene duplication, horizontal gene transfer, and gene loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. However, gene trees are frequently non-binary. With such non-binary gene trees, the reconciliation problem seeks to find a binary resolution of the gene tree that minimizes the reconciliation cost. Given the prevalence of non-binary gene trees, many efficient algorithms have been developed for this problem in the context of the simpler Duplication-Loss (DL) reconciliation model. Yet, no efficient algorithms exist for DTL reconciliation with non-binary gene trees and the complexity of the problem remains unknown. In this work, we resolve this open question by showing that the problem is, in fact, NP-hard. Our reduction applies to both the dated and undated formulations of DTL reconciliation. By resolving this long-standing open problem, this work will spur the development of both exact and heuristic algorithms for this important problem.

Maximizing phylogenetic diversity in biodiversity conservation: Greedy solutions to the Noah's Ark problem.

PubMed

Hartmann, Klaas; Steel, Mike

2006-08-01

The Noah's Ark Problem (NAP) is a comprehensive cost-effectiveness methodology for biodiversity conservation that was introduced by Weitzman (1998) and utilizes the phylogenetic tree containing the taxa of interest to assess biodiversity. Given a set of taxa, each of which has a particular survival probability that can be increased at some cost, the NAP seeks to allocate limited funds to conserving these taxa so that the future expected biodiversity is maximized. Finding optimal solutions using this framework is a computationally difficult problem to which a simple and efficient "greedy" algorithm has been proposed in the literature and applied to conservation problems. We show that, although algorithms of this type cannot produce optimal solutions for the general NAP, there are two restricted scenarios of the NAP for which a greedy algorithm is guaranteed to produce optimal solutions. The first scenario requires the taxa to have equal conservation cost; the second scenario requires an ultrametric tree. The NAP assumes a linear relationship between the funding allocated to conservation of a taxon and the increased survival probability of that taxon. This relationship is briefly investigated and one variation is suggested that can also be solved using a greedy algorithm.

Sensor fusion approaches for EMI and GPR-based subsurface threat identification

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Despite advances in both electromagnetic induction (EMI) and ground penetrating radar (GPR) sensing and related signal processing, neither sensor alone provides a perfect tool for detecting the myriad of possible buried objects that threaten the lives of Soldiers and civilians. However, while neither GPR nor EMI sensing alone can provide optimal detection across all target types, the two approaches are highly complementary. As a result, many landmine systems seek to make use of both sensing modalities simultaneously and fuse the results from both sensors to improve detection performance for targets with widely varying metal content and GPR responses. Despite this, little work has focused on large-scale comparisons of different approaches to sensor fusion and machine learning for combining data from these highly orthogonal phenomenologies. In this work we explore a wide array of pattern recognition techniques for algorithm development and sensor fusion. Results with the ARA Nemesis landmine detection system suggest that nonlinear and non-parametric classification algorithms provide significant performance benefits for single-sensor algorithm development, and that fusion of multiple algorithms can be performed satisfactorily using basic parametric approaches, such as logistic discriminant classification, for the targets under consideration in our data sets.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Stability switches of arbitrary high-order consensus in multiagent networks with time delays.

PubMed

Yang, Bo

2013-01-01

High-order consensus seeking, in which individual high-order dynamic agents share a consistent view of the objectives and the world in a distributed manner, finds its potential broad applications in the field of cooperative control. This paper presents stability switches analysis of arbitrary high-order consensus in multiagent networks with time delays. By employing a frequency domain method, we explicitly derive analytical equations that clarify a rigorous connection between the stability of general high-order consensus and the system parameters such as the network topology, communication time-delays, and feedback gains. Particularly, our results provide a general and a fairly precise notion of how increasing communication time-delay causes the stability switches of consensus. Furthermore, under communication constraints, the stability and robustness problems of consensus algorithms up to third order are discussed in details to illustrate our central results. Numerical examples and simulation results for fourth-order consensus are provided to demonstrate the effectiveness of our theoretical results.

A real time microcomputer implementation of sensor failure detection for turbofan engines

NASA Technical Reports Server (NTRS)

Delaat, John C.; Merrill, Walter C.

1989-01-01

An algorithm was developed which detects, isolates, and accommodates sensor failures using analytical redundancy. The performance of this algorithm was demonstrated on a full-scale F100 turbofan engine. The algorithm was implemented in real-time on a microprocessor-based controls computer which includes parallel processing and high order language programming. Parallel processing was used to achieve the required computational power for the real-time implementation. High order language programming was used in order to reduce the programming and maintenance costs of the algorithm implementation software. The sensor failure algorithm was combined with an existing multivariable control algorithm to give a complete control implementation with sensor analytical redundancy. The real-time microprocessor implementation of the algorithm which resulted in the successful completion of the algorithm engine demonstration, is described.

Design of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

2010-01-01

decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging

On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor Networks

PubMed Central

Campos, Andre N.; Souza, Efren L.; Nakamura, Fabiola G.; Nakamura, Eduardo F.; Rodrigues, Joel J. P. C.

2012-01-01

Target tracking is an important application of wireless sensor networks. The networks' ability to locate and track an object is directed linked to the nodes' ability to locate themselves. Consequently, localization systems are essential for target tracking applications. In addition, sensor networks are often deployed in remote or hostile environments. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. We adapt the density control algorithms to address the k-coverage problem. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime. Our results show that DPE is a better choice for target tracking applications than RPE. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time. PMID:22969329

Coercive control and abused women's decisions about their pets when seeking shelter.

PubMed

Hardesty, Jennifer L; Khaw, Lyndal; Ridgway, Marcella D; Weber, Cheryl; Miles, Teresa

2013-09-01

The importance of pets in families, especially during major life stressors, is well documented. Research suggests links between pet ownership and intimate partner violence (IPV). This study explored abused women's decisions about pets when seeking help from a shelter. Interviews were conducted with 19 women who were pet owners. Using grounded theory methods, two patterns emerged surrounding abusers' treatment of pets, bonds to pets, women's decisions about pets upon seeking shelter, and future plans for pets. The presence of coercive control was central to these patterns. Women also discussed their experiences with and needs from shelter professionals and veterinarians with implications for practice.

Open shop scheduling problem to minimize total weighted completion time

NASA Astrophysics Data System (ADS)

Bai, Danyu; Zhang, Zhihai; Zhang, Qiang; Tang, Mengqian

2017-01-01

A given number of jobs in an open shop scheduling environment must each be processed for given amounts of time on each of a given set of machines in an arbitrary sequence. This study aims to achieve a schedule that minimizes total weighted completion time. Owing to the strong NP-hardness of the problem, the weighted shortest processing time block (WSPTB) heuristic is presented to obtain approximate solutions for large-scale problems. Performance analysis proves the asymptotic optimality of the WSPTB heuristic in the sense of probability limits. The largest weight block rule is provided to seek optimal schedules in polynomial time for a special case. A hybrid discrete differential evolution algorithm is designed to obtain high-quality solutions for moderate-scale problems. Simulation experiments demonstrate the effectiveness of the proposed algorithms.

Analysis and optimization of population annealing

NASA Astrophysics Data System (ADS)

Amey, Christopher; Machta, Jonathan

2018-03-01

Population annealing is an easily parallelizable sequential Monte Carlo algorithm that is well suited for simulating the equilibrium properties of systems with rough free-energy landscapes. In this work we seek to understand and improve the performance of population annealing. We derive several useful relations between quantities that describe the performance of population annealing and use these relations to suggest methods to optimize the algorithm. These optimization methods were tested by performing large-scale simulations of the three-dimensional (3D) Edwards-Anderson (Ising) spin glass and measuring several observables. The optimization methods were found to substantially decrease the amount of computational work necessary as compared to previously used, unoptimized versions of population annealing. We also obtain more accurate values of several important observables for the 3D Edwards-Anderson model.

Faster Parameterized Algorithms for Minor Containment

NASA Astrophysics Data System (ADS)

Adler, Isolde; Dorn, Frederic; Fomin, Fedor V.; Sau, Ignasi; Thilikos, Dimitrios M.

The theory of Graph Minors by Robertson and Seymour is one of the deepest and significant theories in modern Combinatorics. This theory has also a strong impact on the recent development of Algorithms, and several areas, like Parameterized Complexity, have roots in Graph Minors. Until very recently it was a common belief that Graph Minors Theory is mainly of theoretical importance. However, it appears that many deep results from Robertson and Seymour's theory can be also used in the design of practical algorithms. Minor containment testing is one of algorithmically most important and technical parts of the theory, and minor containment in graphs of bounded branchwidth is a basic ingredient of this algorithm. In order to implement minor containment testing on graphs of bounded branchwidth, Hicks [NETWORKS 04] described an algorithm, that in time O(3^{k^2}\\cdot (h+k-1)!\\cdot m) decides if a graph G with m edges and branchwidth k, contains a fixed graph H on h vertices as a minor. That algorithm follows the ideas introduced by Robertson and Seymour in [J'CTSB 95]. In this work we improve the dependence on k of Hicks' result by showing that checking if H is a minor of G can be done in time O(2^{(2k +1 )\\cdot log k} \\cdot h^{2k} \\cdot 2^{2h^2} \\cdot m). Our approach is based on a combinatorial object called rooted packing, which captures the properties of the potential models of subgraphs of H that we seek in our dynamic programming algorithm. This formulation with rooted packings allows us to speed up the algorithm when G is embedded in a fixed surface, obtaining the first single-exponential algorithm for minor containment testing. Namely, it runs in time 2^{O(k)} \\cdot h^{2k} \\cdot 2^{O(h)} \\cdot n, with n = |V(G)|. Finally, we show that slight modifications of our algorithm permit to solve some related problems within the same time bounds, like induced minor or contraction minor containment.

Multi-objective Extremum Seeking Control for Enhancement of Wind Turbine Power Capture with Load Reduction

NASA Astrophysics Data System (ADS)

Xiao, Yan; Li, Yaoyu; Rotea, Mario A.

2016-09-01

The primary objective in below rated wind speed (Region 2) is to maximize the turbine's energy capture. Due to uncertainty, variability of turbine characteristics and lack of inexpensive but precise wind measurements, model-free control strategies that do not use wind measurements such as Extremum Seeking Control (ESC) have received significant attention. Based on a dither-demodulation scheme, ESC can maximize the wind power capture in real time despite uncertainty, variabilities and lack of accurate wind measurements. The existing work on ESC based wind turbine control focuses on power capture only. In this paper, a multi-objective extremum seeking control strategy is proposed to achieve nearly optimum wind energy capture while decreasing structural fatigue loads. The performance index of the ESC combines the rotor power and penalty terms of the standard deviations of selected fatigue load variables. Simulation studies of the proposed multi-objective ESC demonstrate that the damage-equivalent loads of tower and/or blade loads can be reduced with slight compromise in energy capture.

Urban-Rural Differences in Health-Care-Seeking Pattern of Residents of Abia State, Nigeria, and the Implication in the Control of NCDs.

PubMed

Onyeonoro, Ugochukwu U; Ogah, Okechukwu S; Ukegbu, Andrew U; Chukwuonye, Innocent I; Madukwe, Okechukwu O; Moses, Akhimiem O

2016-01-01

Understanding the differences in care-seeking pattern is key in designing interventions aimed at improving health-care service delivery, including prevention and control of noncommunicable diseases. The aim of this study was to identify the differences and determinants of care-seeking patterns of urban and rural residents in Abia State in southeast Nigeria. This was a cross-sectional, community-based, study involving 2999 respondents aged 18 years and above. Data were collected using the modified World Health Organization's STEPS questionnaire, including data on care seeking following the onset of illness. Descriptive statistics and logistic regressions were used to analyze care-seeking behavior and to identify differences among those seeking care in urban and rural areas. In both urban and rural areas, patent medicine vendors (73.0%) were the most common sources of primary care following the onset of illness, while only 20.0% of the participants used formal care. Significant predictors of difference in care-seeking practices between residents in urban and rural communities were educational status, income, occupation, and body mass index. Efforts should be made to reduce barriers to formal health-care service utilization in the state by increasing health insurance coverage, strengthening the health-care system, and increasing the role of patent medicine vendors in the formal health-care delivery system.

Urban–Rural Differences in Health-Care-Seeking Pattern of Residents of Abia State, Nigeria, and the Implication in the Control of NCDs

PubMed Central

Onyeonoro, Ugochukwu U.; Ogah, Okechukwu S.; Ukegbu, Andrew U.; Chukwuonye, Innocent I.; Madukwe, Okechukwu O.; Moses, Akhimiem O.

2016-01-01

BACKGROUND Understanding the differences in care-seeking pattern is key in designing interventions aimed at improving health-care service delivery, including prevention and control of noncommunicable diseases. The aim of this study was to identify the differences and determinants of care-seeking patterns of urban and rural residents in Abia State in southeast Nigeria. METHODS This was a cross-sectional, community-based, study involving 2999 respondents aged 18 years and above. Data were collected using the modified World Health Organization’s STEPS questionnaire, including data on care seeking following the onset of illness. Descriptive statistics and logistic regressions were used to analyze care-seeking behavior and to identify differences among those seeking care in urban and rural areas. RESULTS In both urban and rural areas, patent medicine vendors (73.0%) were the most common sources of primary care following the onset of illness, while only 20.0% of the participants used formal care. Significant predictors of difference in care-seeking practices between residents in urban and rural communities were educational status, income, occupation, and body mass index. CONCLUSIONS Efforts should be made to reduce barriers to formal health-care service utilization in the state by increasing health insurance coverage, strengthening the health-care system, and increasing the role of patent medicine vendors in the formal health-care delivery system. PMID:27721654

Theory of Planned Behavior including self-stigma and perceived barriers explain help-seeking behavior for sexual problems in Iranian women suffering from epilepsy.

PubMed

Lin, Chung-Ying; Oveisi, Sonia; Burri, Andrea; Pakpour, Amir H

2017-03-01

To apply the Theory of Planned Behavior (TPB) and the two additional concepts self-stigma and perceived barriers to the help-seeking behavior for sexual problems in women with epilepsy. In this 18-month follow-up study, TPB elements, including attitude, subjective norm, perceived behavioral control, and behavioral intention along with self-stigma and perceived barriers in seeking help for sexual problems were assessed in n=818 women with epilepsy (94.0% aged ≤40years). The basic TPB model (model 1) and the TPB model additionally including self-stigma and perceived barriers (Model 2) were analyzed using structural equation modeling (SEM). Both SEM models showed satisfactory model fits. According to model, attitude, subjective norms, perceived behavioral control, and intention explained 63.1% of the variance in help-seeking behavior. Variance was slightly higher (64.5%) when including self-stigma and perceived barriers (model 2). In addition, the fit indices of the models were better highlighting the importance of self-stigma and perceived barriers in help-seeking behavior for sexual problems. Theory of Planned Behavior is useful in explaining help-seeking behavior for sexual problems in women with epilepsy. Self-stigma and perceived barriers are additional factors that should be considered in future interventions aiming to adopt TPB to improve help-seeking behavior for sexual problems. Copyright © 2017 Elsevier Inc. All rights reserved.

Outpatient Mental Health Treatment Utilization and Military Career Impact in the United States Marine Corps

PubMed Central

Ghahramanlou-Holloway, Marjan; LaCroix, Jessica M.; Koss, Kari; Perera, Kanchana U.; VanSickle, Marcus R.; Novak, Laura A.

2018-01-01

Service members (SM) are at increased risk of psychiatric conditions, including suicide, yet research indicates SMs believe seeking mental health treatment may negatively impact their military careers, despite a paucity of research examining actual career impacts. This study examined the link between seeking outpatient mental health (MH) treatment and military career impacts within the United States Marine Corps. In Phase 1, a retrospective medical record review of outpatient MH treatment-seeking Marines (N = 38) was conducted. In Phase 2, a sample of outpatient MH treatment-seeking Marines (N = 40) was matched to a non-treatment-seeking sample of Marines (N = 138) to compare career-progression. In Phase 1, there were no significant links between demographic, military, and clinical characteristics and referral source or receipt of career-affecting treatment recommendations. In Phase 2, MH treatment-seeking Marines in outpatient settings were more likely than matched controls to be separated from the military (95.0% versus 63.0%, p = 0.002), but no more likely to experience involuntary separation. MH treatment-seeking Marines were more likely to have documented legal action (45.0% versus 23.9%, p = 0.008) and had a shorter time of military service following the index MH encounter than matched controls (p < 0.001). Clinical, anti-stigma, and suicide prevention policy implications are discussed. PMID:29690594

An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication

NASA Astrophysics Data System (ADS)

Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao

2014-05-01

For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

NASA Astrophysics Data System (ADS)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach are less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

Layout optimization using the homogenization method

NASA Technical Reports Server (NTRS)

Suzuki, Katsuyuki; Kikuchi, Noboru

1993-01-01

A generalized layout problem involving sizing, shape, and topology optimization is solved by using the homogenization method for three-dimensional linearly elastic shell structures in order to seek a possibility of establishment of an integrated design system of automotive car bodies, as an extension of the previous work by Bendsoe and Kikuchi. A formulation of a three-dimensional homogenized shell, a solution algorithm, and several examples of computing the optimum layout are presented in this first part of the two articles.

The Veterans Health Administration’s Treatment of PTSD and Traumatic Brain Injury Among Recent Combat Veterans

DTIC Science & Technology

2012-02-01

make it more difficult for veterans with PTSD to seek or maintain treatment. VHA provides treatment for PTSD at VHA hospitals , outpatient clinics ...measured in days of inpatient hospital care and outpatient clinic visits. A veteran may have had several outpatient visits on a sin- gle day, each...reproduce the same results precisely. The DSS system takes clinical and financial information from other VHA databases and uses algorithms that merge

The Center for Advanced Systems and Engineering (CASE). Visiting Faculty Research Program 06 March 2007 to 05 March 2009

DTIC Science & Technology

2009-09-01

and could be used to compensate for high frequency distortions to the LOS caused by platform jitter and the effects of the optical turbulence . In...engineer an unknown detector based on few experimental interactions. For watermarking algorithms in particular, we seek to identify specific distortions ...of a watermarked image that clearly identify or rule out one particular class of embedding. These experimental distortions surgical test for rapid

Predicting help-seeking behavior: The impact of knowing someone close who has sought help.

PubMed

Disabato, David J; Short, Jerome L; Lameira, Diane M; Bagley, Karen D; Wong, Stephanie J

2018-02-15

This study sought to replicate and extend research on social facilitators of college student's help seeking for psychological problems. We collected data on 420 ethnically diverse college students at a large public university (September 2008-May 2010). Students completed a cross-sectional online survey. We found that students who were aware of close others' (eg, family, friends) help seeking were two times more likely to have sought formal (eg, psychologist) and informal (eg, clergy) help themselves. Tests of moderation revealed the incremental effect (ie, controlling for help-seeking attitudes, internalizing symptoms, cultural demographics) of close others' formal help seeking was strong and significant for men (R 2 = 0.112), while it was negligible and nonsignificant for women (R 2 = .002). We discuss the importance for students-particularly men-to learn about close others' help seeking for facilitating their own help seeking during times of distress.

Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health status into QALY-weights.

PubMed

Mortimer, Duncan; Segal, Leonie

2008-01-01

Algorithms for converting descriptive measures of health status into quality-adjusted life year (QALY)--weights are now widely available, and their application in economic evaluation is increasingly commonplace. The objective of this study is to describe and compare existing conversion algorithms and to highlight issues bearing on the derivation and interpretation of the QALY-weights so obtained. Systematic review of algorithms for converting descriptive measures of health status into QALY-weights. The review identified a substantial body of literature comprising 46 derivation studies and 16 studies that provided evidence or commentary on the validity of conversion algorithms. Conversion algorithms were derived using 1 of 4 techniques: 1) transfer to utility regression, 2) response mapping, 3) effect size translation, and 4) "revaluing" outcome measures using preference-based scaling techniques. Although these techniques differ in their methodological/theoretical tradition, data requirements, and ease of derivation and application, the available evidence suggests that the sensitivity and validity of derived QALY-weights may be more dependent on the coverage and sensitivity of measures and the disease area/patient group under evaluation than on the technique used in derivation. Despite the recent proliferation of conversion algorithms, a number of questions bearing on the derivation and interpretation of derived QALY-weights remain unresolved. These unresolved issues suggest directions for future research in this area. In the meantime, analysts seeking guidance in selecting derived QALY-weights should consider the validity and feasibility of each conversion algorithm in the disease area and patient group under evaluation rather than restricting their choice to weights from a particular derivation technique.

Sensation Seeking and Locus of Control in University Students in the Context of Regular Exercise Participation and Gender

ERIC Educational Resources Information Center

Tekin, Ali; Tekin, Gülcan; Çalisir, Melih

2017-01-01

The aim of this study is to determine the locus of control (LC) and sensation seeking (SS) levels of university female students according to regular exercise participation (REP) and gender (G). This descriptive study was initiated in 2016 and finished in 2017. A total of 623 students, 306 females and 317 males, from different academic departments…

Transform methods for precision continuum and control models of flexible space structures

NASA Technical Reports Server (NTRS)

Lupi, Victor D.; Turner, James D.; Chun, Hon M.

1991-01-01

An open loop optimal control algorithm is developed for general flexible structures, based on Laplace transform methods. A distributed parameter model of the structure is first presented, followed by a derivation of the optimal control algorithm. The control inputs are expressed in terms of their Fourier series expansions, so that a numerical solution can be easily obtained. The algorithm deals directly with the transcendental transfer functions from control inputs to outputs of interest, and structural deformation penalties, as well as penalties on control effort, are included in the formulation. The algorithm is applied to several structures of increasing complexity to show its generality.

Control algorithms for dynamic windows for residential buildings

DOE PAGES

Firlag, Szymon; Yazdanian, Mehrangiz; Curcija, Charlie; ...

2015-09-30

This study analyzes the influence of control algorithms for dynamic windows on energy consumption, number of hours of retracted shades during daylight and shade operations. Five different control algorithms - heating/cooling, simple rules, perfect citizen, heat flow and predictive weather were developed and compared. The performance of a typical residential building was modeled with EnergyPlus. The program Widow was used to generate a Bi-Directional Distribution Function (BSDF) for two window configurations. The BSDF was exported to EnergyPlus using the IDF file format. The EMS feature in EnergyPlus was used to develop custom control algorithms. The calculations were made for fourmore » locations with diverse climate. The results showed that: (a) use of automated shading with proposed control algorithms can reduce the site energy in the range of 11.6-13.0%; in regard to source (primary) energy in the range of 20.1-21.6%, (b) the differences between algorithms in regard to energy savings are not high, (c) the differences between algorithms in regard to number of hours of retracted shades are visible, (e) the control algorithms have a strong influence on shade operation and oscillation of shade can occur, (d) additional energy consumption caused by motor, sensors and a small microprocessor in the analyzed case is very small.« less

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

Diminution of Heart Rate Variability in Bipolar Depression

PubMed Central

Hage, Brandon; Britton, Briana; Daniels, David; Heilman, Keri; Porges, Stephen W.; Halaris, Angelos

2017-01-01

Autonomic nervous system (ANS) dysregulation in depression is associated with symptoms associated with the ANS. The beat-to-beat pattern of heart rate defined as heart rate variability (HRV) provides a noninvasive portal to ANS function and has been proposed to represent a means of quantifying resting vagal tone. We quantified HRV in bipolar depressed (BDD) patients as a measure of ANS dysregulation seeking to establish HRV as a potential diagnostic and prognostic biomarker for treatment outcome. Forty-seven BDD patients were enrolled. They were randomized to receive either escitalopram–celecoxib or escitalopram-placebo over 8 weeks in a double-blind study design. Thirty-five patients completed the HRV studies. Thirty-six healthy subjects served as controls. HRV was assessed at pretreatment and end of study and compared with that of controls. HRV was quantified and corrected for artifacts using an algorithm that incorporates time and frequency domains to address non-stationarity of the beat-to-beat heart rate pattern. Baseline high frequency-HRV (i.e., respiratory sinus arrhythmia) was lower in BDD patients than controls, although the difference did not reach significance. Baseline low-frequency HRV was significantly lower in BDD patients (ln4.20) than controls (ln = 5.50) (p < 0.01). Baseline heart period was significantly shorter (i.e., faster heart rate) in BDD patients than controls. No significant change in HRV parameters were detected over the course of the study with either treatment. These findings suggest that components of HRV may be diminished in BDD patients. PMID:29270399

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685

Motor Control and Regulation for a Flywheel Energy Storage System

NASA Technical Reports Server (NTRS)

Kenny, Barbara; Lyons, Valerie

2003-01-01

This talk will focus on the motor control algorithms used to regulate the flywheel system at the NASA Glenn Research Center. First a discussion of the inner loop torque control technique will be given. It is based on the principle of field orientation and is implemented without a position or speed sensor (sensorless control). Then the outer loop charge and discharge algorithm will be presented. This algorithm controls the acceleration of the flywheel during charging and the deceleration while discharging. The algorithm also allows the flywheel system to regulate the DC bus voltage during the discharge cycle.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

NASA Astrophysics Data System (ADS)

Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun

2015-09-01

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Compulsive Sexual Behavior

MedlinePlus

... to hurt yourself or someone else, you report sexual abuse of a child, or you report abuse or neglect of someone in a vulnerable population. Seek treatment right away Seek immediate ... uncontrolled sexual behavior You have other problems with impulse control, ...

Elder American Indian women's knowledge of pelvic floor disorders and barriers to seeking care.

PubMed

Dunivan, Gena C; Komesu, Yuko M; Cichowski, Sara B; Lowery, Christine; Anger, Jennifer T; Rogers, Rebecca G

2015-01-01

The objectives of this study are to evaluate urinary incontinence and pelvic organ prolapse knowledge among elder southwestern American Indian women and to assess barriers to care for pelvic floor disorders through community-engaged research. Our group was invited to provide an educational talk on urinary incontinence and pelvic organ prolapse at an annual meeting of American Indian elders. Female attendees aged 55 years or older anonymously completed demographic information and 2 validated questionnaires, the Prolapse and Incontinence Knowledge Questionnaire (PIKQ) and Barriers to Incontinence Care Seeking Questionnaire (BICS-Q). Questionnaire results were compared with historical controls from the original PIKQ and BICS-Q validation study. One hundred forty-four women completed the questionnaires. The mean age was 77.7 ± 9.1 years. The mean (SD) for PIKQ of urinary incontinence score was 6.6 (3.0) (similar to historic gynecology controls 6.8 [3.3], P = 0.49), and the mean (SD) for PIKQ on pelvic organ prolapse score was 5.4 (2.9) (better than historic gynecology controls 3.6 [3.2], P < 0.01). Barriers to care seeking reported by the elder women were highest on the BICS-Q subscales of "cost" and "inconvenience." Urinary incontinence knowledge is similar to historic gynecology controls, and pelvic organ prolapse knowledge is higher than historic gynecology controls among elder southwestern American Indian women. American Indian elder women report high levels of barriers to care. The greatest barriers to care seeking for this population were related to cost and inconvenience, reflecting the importance of assessing socioeconomic status when investigating barriers to care. Addressing these barriers may enhance care-seeking southwestern American Indian women.

A novel highly selective 5-HT6 receptor antagonist attenuates ethanol and nicotine seeking but does not affect inhibitory response control in Wistar rats.

PubMed

de Bruin, N M W J; McCreary, A C; van Loevezijn, A; de Vries, T J; Venhorst, J; van Drimmelen, M; Kruse, C G

2013-01-01

Recent studies suggest a potential role for 5-hydroxytryptamine(6) (5-HT(6)) receptors in the regulation of addictive behavior. In the present study, our aim was to investigate whether the novel highly selective 5-HT(6) receptor antagonist compound (CMP) 42 affected nicotine and ethanol seeking behavior in Wistar rats. We have also studied whether CMP 42 had beneficial effects in a model of impulse control, as measured in the 5-choice serial reaction time task (5-CSRTT). Rats were trained to nose poke to receive intravenous infusions of nicotine or an ethanol drop. CMP 42 (3-30 mg/kg intraperitoneally, i.p.) was administered to investigate the effects on nicotine self-administration. Rats were also tested for cue-induced reinstatement of nicotine and ethanol seeking. In addition, the effects of CMP 42 were studied on the number of anticipatory responses in the 5-CSRTT. CMP 42 was effective in reducing nicotine self-administration and reinstatement of nicotine seeking at a dose of 30 mg/kg (i.p.). CMP 42 was also effective in reducing reinstatement of ethanol seeking (30 mg/kg i.p.). In contrast, CMP 42 did not affect anticipatory responding at doses tested, indicating no effects on impulse control. These results add to a body of evidence implicating the 5-HT(6) receptor as a viable target for the control of drug abuse. Specifically, we demonstrated for the first time effects on nicotine self-administration and on nicotine and ethanol reinstatement. Further, these effects are probably not mediated by effects on impulse control. Copyright © 2012 Elsevier B.V. All rights reserved.

[Changes of telemetry electrical activity in the infralimbic cortex of morphine-dependent rats with extinguished drug-seeking behavior].

PubMed

Li, Jing; Pan, Qunwan; Zhu, Zaiman; Li, Min; Bai, Yu; Yu, Ran

2015-05-01

To investigate the changes of telemetry electrical activity in the infralimbic cortex (IL) of morphine-dependent rats with extinguished drug-seeking behavior. SD rats were randomly divided into model group and control group and received operations of brain stereotaxic electrode embedding in the IL. The rats in the model group were induced to acquire morphine dependence and then received subsequent extinction training, and the changes of electrical activity in the IL were recorded with a physical wireless telemetry system. In rats with morphine dependence, the time staying in the white box was significantly longer on days 1 and 2 after withdrawal than that before morphine injection and that of the control rats, but was obviously reduced on days 1 and 2 after extinction training to the control level. Compared with the control group, the morphine-dependent rats on day 2 following withdrawal showed significantly increased β wave and decreased δ wave when they stayed in the white box but significantly increased δ wave and decreased α wave and β wave when they shuttled from the black to the white box. On day 2 of extinction, the model rats, when staying in the white box, showed significantly decreased θ wave compared with that of the control rats group but decreased β wave and θ wave and increased δ wave compared with those in the withdrawal period. When they shuttled from black to white box, the model rats showed decreased δ wave and increased α wave and β wave compared with those in the withdrawal period. Morphine-dependent rats have abnormal changes of electrical activity in the IL in drug-seeking extinction to affect their drug-seeking motive and inhibit the expression and maintenance of drug-seeking behaviors.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Food seeking in spite of harmful consequences is under prefrontal cortical noradrenergic control

PubMed Central

2010-01-01

Background Eating disorders are multifactorial psychiatric disorders. Chronic stressful experiences and caloric restriction are the most powerful triggers of eating disorders in human and animals. Although compulsive behavior is considered to characterize pathological excessive food intake, to our knowledge, no evidence has been reported of continued food seeking/intake despite its possible harmful consequences, an index of compulsive behavior. Brain monoamine transmission is considered to have a key role in vulnerability to eating disorders, and norepinephrine in medial prefrontal cortex has been shown to be critical for food-related motivated behavior. Here, using a new paradigm of conditioned suppression, we investigated whether the ability of a foot-shock-paired conditioned stimulus to suppress chocolate-seeking behavior was reversed by previous exposure to a food restriction experience, thus modeling food seeking in spite of harmful consequences in mice. Moreover, we assessed the effects of selective norepinephrine inactivation in medial prefrontal cortex on conditioned suppression test in stressed and caloric restricted mice. Results While Control (non food deprived) animals showed a profound conditioned suppression of chocolate seeking during presentation of conditioned stimulus, previously food restricted animals showed food seeking/intake despite its possible harmful consequences. Moreover, food seeking in spite of harmful consequences was prevented by selective norepinephrine inactivation, thus showing that prefrontal cortical norepinephrine is critical also for maladaptive food-related behavior. Conclusions These findings indicate that adaptive food seeking/intake can be transformed into maladaptive behaviors and point to "top-down" influence on eating disturbances and to new targets for therapy of aberrant eating behaviors. PMID:20141625

Clinical Characteristics of Men Interested in Seeking Treatment for Use of Pornography

PubMed Central

Kraus, Shane W.; Martino, Steve; Potenza, Marc N.

2016-01-01

Background and aims This study examined the prevalence of, and factors associated with, men’s interest in seeking treatment for use of pornography. Methods Using an Internet-based data-collection procedure, we recruited 1,298 male pornography users to complete questionnaires assessing demographic and sexual behaviors, hypersexuality, pornography-use characteristics, and current interest in seeking treatment for use of pornography. Results Approximately 14% of men reported an interest in seeking treatment for use of pornography, whereas only 6.4% of men had previously sought treatment for use of pornography. Treatment-interested men were 9.5 times more likely to report clinically significant levels of hypersexuality compared with treatment-disinterested men (OR = 9.52, 95% CI = 6.72–13.49). Bivariate analyses indicated that interest-in-seeking-treatment status was associated with being single/unmarried, viewing more pornography per week, engaging in more solitary masturbation in the past month, having had less dyadic oral sex in the past month, reporting a history of seeking treatment for use of pornography, and having had more past attempts to either “cut back” or quit using pornography completely. Results from a binary logistic regression analysis indicated that more frequent cut back/quit attempts with pornography and scores on the Hypersexual Behavior Inventory – Control subscale were significant predictors of interest-in-seeking-treatment status. Discussion and conclusions Study findings could be used to inform current screening practices aimed at identifying specific aspects of sexual self-control, impulsivity, and/or compulsivity associated with problematic use of pornography among treatment-seeking individuals. PMID:27348557

Clinical Characteristics of Men Interested in Seeking Treatment for Use of Pornography.

PubMed

Kraus, Shane W; Martino, Steve; Potenza, Marc N

2016-06-01

Background and aims This study examined the prevalence of, and factors associated with, men's interest in seeking treatment for use of pornography. Methods Using an Internet-based data-collection procedure, we recruited 1,298 male pornography users to complete questionnaires assessing demographic and sexual behaviors, hypersexuality, pornography-use characteristics, and current interest in seeking treatment for use of pornography. Results Approximately 14% of men reported an interest in seeking treatment for use of pornography, whereas only 6.4% of men had previously sought treatment for use of pornography. Treatment-interested men were 9.5 times more likely to report clinically significant levels of hypersexuality compared with treatment-disinterested men (OR = 9.52, 95% CI = 6.72-13.49). Bivariate analyses indicated that interest-in-seeking-treatment status was associated with being single/unmarried, viewing more pornography per week, engaging in more solitary masturbation in the past month, having had less dyadic oral sex in the past month, reporting a history of seeking treatment for use of pornography, and having had more past attempts to either "cut back" or quit using pornography completely. Results from a binary logistic regression analysis indicated that more frequent cut back/quit attempts with pornography and scores on the Hypersexual Behavior Inventory - Control subscale were significant predictors of interest-in-seeking-treatment status. Discussion and conclusions Study findings could be used to inform current screening practices aimed at identifying specific aspects of sexual self-control, impulsivity, and/or compulsivity associated with problematic use of pornography among treatment-seeking individuals.

Trust metrics in information fusion

NASA Astrophysics Data System (ADS)

Blasch, Erik

2014-05-01

Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust information in the identified imagery track. We use the proportional conflict redistribution rule as an information fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for information fusion trust quantification.

Towards the Engineering of Dependable P2P-Based Network Control — The Case of Timely Routing Control Messages

NASA Astrophysics Data System (ADS)

Tutschku, Kurt; Nakao, Akihiro

This paper introduces a methodology for engineering best-effort P2P algorithms into dependable P2P-based network control mechanism. The proposed method is built upon an iterative approach consisting of improving the original P2P algorithm by appropriate mechanisms and of thorough performance assessment with respect to dependability measures. The potential of the methodology is outlined by the example of timely routing control for vertical handover in B3G wireless networks. In detail, the well-known Pastry and CAN algorithms are enhanced to include locality. By showing how to combine algorithmic enhancements with performance indicators, this case study paves the way for future engineering of dependable network control mechanisms through P2P algorithms.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Masculinity and race-related factors as barriers to health help-seeking barriers among African-American men

PubMed Central

Powell, Wizdom; Adams, Leslie B.; Cole-Lewis, Yasmin; Agyemang, Amma; Upton, Rachel D.

2016-01-01

Men’s tendency to delay health help-seeking is largely attributed to masculinity, but findings scarcely focus on African-American men who face additional race-related, help-seeking barriers. Building principally on reactance theory, we test a hypothesized model situating racial discrimination, masculinity norms salience, everyday racism (ERD), racial identity (RI), sense of control (SOC) and depressive symptomatology as key barriers to African-American men’s health help-seeking. 458 African-American men were recruited primarily from U.S. barbershops in the Western and Southern regions. The primary outcome was Barriers to Help-Seeking Scale (BHSS) scores. The hypothesized model was investigated with confirmatory factor and path analysis with tests for measurement invariance. Our model fit was excellent χ2(4,N = 457) = 3.84, p > .05; CFI = .99; TLI = 1.00; RMSEA = .00, and 90% CI [.00, .07] and operated equivalently across different age, income, and education strata. Frequent ERD and higher MNS contributed to higher BHHS scores. The relationship between ERD exposure and BHHS scores was partially mediated by diminished SOC and greater depressive symptomatology. Interventions aimed at addressing African-American men’s health help-seeking should not only address masculinity norms, but also threats to sense of control, and negative psychological sequelae induced by everyday racism. PMID:27337619

Masculinity and Race-Related Factors as Barriers to Health Help-Seeking Among African American Men.

PubMed

Powell, Wizdom; Adams, Leslie B; Cole-Lewis, Yasmin; Agyemang, Amma; Upton, Rachel D

2016-01-01

Men's tendency to delay health help-seeking is largely attributed to masculinity, but findings scarcely focus on African American men who face additional race-related, help-seeking barriers. Building principally on reactance theory, we test a hypothesized model situating racial discrimination, masculinity norms salience (MNS), everyday racism (ERD), racial identity, sense of control (SOC), and depressive symptomatology as key barriers to African American men's health help-seeking. A total of 458 African American men were recruited primarily from US barbershops in the Western and Southern regions. The primary outcome was Barriers to Help-Seeking Scale (BHSS) scores. The hypothesized model was investigated with confirmatory factor and path analysis with tests for measurement invariance. Our model fit was excellent [Formula: see text] CFI = 0.99; TLI = 1.00; RMSEA = 0.00, and 90% CI [0.00, 0.07] and operated equivalently across different age, income, and education strata. Frequent ERD and higher MNS contributed to higher BHHS scores. The relationship between ERD exposure and BHHS scores was partially mediated by diminished SOC and greater depressive symptomatology. Interventions aimed at addressing African American men's health help-seeking should not only address masculinity norms but also threats to sense of control, and negative psychological sequelae induced by everyday racism.

Psychiatric morbidity in asymptomatic human immunodeficiency virus patients.

PubMed

Chauhan, V S; Chaudhury, Suprakash; Sudarsanan, S; Srivastava, Kalpana

2013-07-01

Psychiatric morbidity in human immunodeficiency virus (HIV) patients is being studied all over the world. There is paucity of Indian literature particularly in asymptomatic HIV individuals. The aim of the following study is to establish the prevalence and the determinants of psychiatric morbidity in asymptomatic HIV patients. A cross-sectional study was undertaken to assess psychiatric morbidity as per ICD-10 dacryocystorhinostomy criteria in 100 consecutive asymptomatic seropositive HIV patients and an equal number of age, sex, education, economic and marital status matched HIV seronegative control. All subjects were assessed with the general health questionnaire (GHQ), mini mental status examination, hospital anxiety and depression scale (HADS) and sensation seeking scale (SSS) and the scores were analyzed statistically. Asymptomatic HIV positive patients had significantly higher GHQ caseness and depression but not anxiety on HADS as compared to HIV seronegative controls. On SSS asymptomatic HIV seropositive subjects showed significant higher scores in thrill and adventure seeking, experience seeking and boredom susceptibility as compared to controls. HIV seropositive patients had significantly higher incidence of total psychiatric morbidity. Among the individual disorders, alcohol dependence syndrome, sexual dysfunction and adjustment disorder were significantly increased compared with HIV seronegative controls. Psychiatric morbidity is higher in asymptomatic HIV patients when compared to HIV seronegative controls. Among the individual disorders, alcohol dependence syndrome, sexual dysfunction and adjustment disorder were significantly increased compared with HIV seronegative controls. High sensation seeking and substance abuse found in HIV seropositive patients may play a vital role in engaging in high-risk behavior resulting in this dreaded illness.

Continuous intensity map optimization (CIMO): A novel approach to leaf sequencing in step and shoot IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao Daliang; Earl, Matthew A.; Luan, Shuang

2006-04-15

A new leaf-sequencing approach has been developed that is designed to reduce the number of required beam segments for step-and-shoot intensity modulated radiation therapy (IMRT). This approach to leaf sequencing is called continuous-intensity-map-optimization (CIMO). Using a simulated annealing algorithm, CIMO seeks to minimize differences between the optimized and sequenced intensity maps. Two distinguishing features of the CIMO algorithm are (1) CIMO does not require that each optimized intensity map be clustered into discrete levels and (2) CIMO is not rule-based but rather simultaneously optimizes both the aperture shapes and weights. To test the CIMO algorithm, ten IMRT patient cases weremore » selected (four head-and-neck, two pancreas, two prostate, one brain, and one pelvis). For each case, the optimized intensity maps were extracted from the Pinnacle{sup 3} treatment planning system. The CIMO algorithm was applied, and the optimized aperture shapes and weights were loaded back into Pinnacle. A final dose calculation was performed using Pinnacle's convolution/superposition based dose calculation. On average, the CIMO algorithm provided a 54% reduction in the number of beam segments as compared with Pinnacle's leaf sequencer. The plans sequenced using the CIMO algorithm also provided improved target dose uniformity and a reduced discrepancy between the optimized and sequenced intensity maps. For ten clinical intensity maps, comparisons were performed between the CIMO algorithm and the power-of-two reduction algorithm of Xia and Verhey [Med. Phys. 25(8), 1424-1434 (1998)]. When the constraints of a Varian Millennium multileaf collimator were applied, the CIMO algorithm resulted in a 26% reduction in the number of segments. For an Elekta multileaf collimator, the CIMO algorithm resulted in a 67% reduction in the number of segments. An average leaf sequencing time of less than one minute per beam was observed.« less

The Sources of Strength Australia Project: study protocol for a cluster randomised controlled trial.

PubMed

Calear, Alison L; Brewer, Jacqueline L; Batterham, Philip J; Mackinnon, Andrew; Wyman, Peter A; LoMurray, Mark; Shand, Fiona; Kazan, Dominique; Christensen, Helen

2016-07-26

The school system has been identified as an ideal setting for the implementation of prevention and early intervention programs for suicide. However, in Australia, suicide-prevention programs that are routinely delivered in the schools are lacking. Internationally, evidence exists for the effectiveness of peer-led interventions that take a social connectedness approach to improve help-seeking for suicide. The aim of the current trial is to test the effectiveness of the Sources of Strength program to promote help-seeking for suicide in adolescents in Australian high schools. This study is a two-arm, cluster-randomised, controlled trial that will compare the evidence-based Sources of Strength program to a wait-list control condition. Sixteen Australian high schools will be recruited to the trial, with all adolescents in years 7 to 10 (12-16 years of age) invited to participate. Peer leaders from intervention-condition schools will receive training in the Sources of Strength program and will integrate positive messages, across 3 months, with the support of adult advisors. Activities may take the form of class presentations, posters, videos, and messages on social media sites and will aim to change help-seeking norms, strengthen youth-adult connections, and promote positive coping. The primary outcome measure for the study is help-seeking intentions, whereas secondary outcomes include help-seeking behaviour, help-seeking attitudes and norms, referral of distressed peers, availability of adult help, positive coping, and suicidal behaviour. Data will be collected pre-intervention, post-intervention (after the initial 3 months of messaging), and at the end of the first (6-month follow-up) and the second year after implementation (18-month follow-up). Primary analyses will compare changes in help-seeking intentions for the intervention condition relative to the wait-list control condition using mixed-effect repeated-measures analyses to account for clustering within schools. If proven effective, this universal social connectedness program for suicide could be more widely delivered in Australian high schools, providing a valuable new resource. The Sources of Strength program has the potential to significantly contribute to the mental health of young people in Australia by improving help-seeking for suicide. The findings from this research will also contribute to the evidence-base for peer-leadership programs internationally. Australian New Zealand Clinical Trials Registry, ACTRN12616000048482 . Registered on 19 January 2016.

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

NASA Technical Reports Server (NTRS)

Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

Facilitating change in health-related behaviors and intentions: a randomized controlled trial of a multidimensional memory program for older adults.

PubMed

Wiegand, Melanie A; Troyer, Angela K; Gojmerac, Christina; Murphy, Kelly J

2013-01-01

Many older adults are concerned about memory changes with age and consequently seek ways to optimize their memory function. Memory programs are known to be variably effective in improving memory knowledge, other aspects of metamemory, and/or objective memory, but little is known about their impact on implementing and sustaining lifestyle and healthcare-seeking intentions and behaviors. We evaluated a multidimensional, evidence-based intervention, the Memory and Aging Program, that provides education about memory and memory change, training in the use of practical memory strategies, and support for implementation of healthy lifestyle behavior changes. In a randomized controlled trial, 42 healthy older adults participated in a program (n = 21) or a waitlist control (n = 21) group. Relative to the control group, participants in the program implemented more healthy lifestyle behaviors by the end of the program and maintained these changes 1 month later. Similarly, program participants reported a decreased intention to seek unnecessary medical attention for their memory immediately after the program and 1 month later. Findings support the use of multidimensional memory programs to promote healthy lifestyles and influence healthcare-seeking behaviors. Discussion focuses on implications of these changes for maximizing cognitive health and minimizing impact on healthcare resources.

Fast-kick-off monotonically convergent algorithm for searching optimal control fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liao, Sheng-Lun; Ho, Tak-San; Rabitz, Herschel

2011-09-15

This Rapid Communication presents a fast-kick-off search algorithm for quickly finding optimal control fields in the state-to-state transition probability control problems, especially those with poorly chosen initial control fields. The algorithm is based on a recently formulated monotonically convergent scheme [T.-S. Ho and H. Rabitz, Phys. Rev. E 82, 026703 (2010)]. Specifically, the local temporal refinement of the control field at each iteration is weighted by a fractional inverse power of the instantaneous overlap of the backward-propagating wave function, associated with the target state and the control field from the previous iteration, and the forward-propagating wave function, associated with themore » initial state and the concurrently refining control field. Extensive numerical simulations for controls of vibrational transitions and ultrafast electron tunneling show that the new algorithm not only greatly improves the search efficiency but also is able to attain good monotonic convergence quality when further frequency constraints are required. The algorithm is particularly effective when the corresponding control dynamics involves a large number of energy levels or ultrashort control pulses.« less

Strengthening malaria service delivery through supportive supervision and community mobilization in an endemic Indian setting: an evaluation of nested delivery models.

PubMed

Das, Ashis; Friedman, Jed; Kandpal, Eeshani; Ramana, Gandham N V; Gupta, Rudra Kumar Das; Pradhan, Madan M; Govindaraj, Ramesh

2014-12-08

Malaria continues to be a prominent global public health challenge. This study tested the effectiveness of two service delivery models for reducing the malaria burden, e.g. supportive supervision of community health workers (CHW) and community mobilization in promoting appropriate health-seeking behaviour for febrile illnesses in Odisha, India. The study population comprised 120 villages from two purposively chosen malaria-endemic districts, with 40 villages randomly assigned to each of the two treatment arms, one with both supportive supervision and community mobilization and one with community mobilization alone, as well as an observational control arm. Outcome measures included changes in the utilization of bed nets and timely care-seeking for fever from a trained provider compared to the control group. Analysis was by intention-to-treat. Significant improvements were observed in the reported utilization of bed nets in both intervention arms (84.5% in arm A and 82.4% in arm B versus 78.6% in the control arm; p < 0.001). While overall rates of treatment-seeking were equal across study arms, treatment-seeking from a CHW was higher in both intervention arms (28%; p = 0.005 and 27.6%; p = 0.007) than in the control arm (19.2%). Fever cases were significantly more likely to visit a CHW and receive a timely diagnosis of fever in the combined interventions arm than in the control arm (82.1% vs. 67.1%; p = 0.025). Care-seeking from trained providers also increased with a substitution away from untrained providers. Further, fever cases from the combined interventions arm (60.6%; p = 0.004) and the community mobilization arm (59.3%; p = 0.012) were more likely to have received treatment from a skilled provider within 24 hours than fever cases from the control arm (50.1%). In particular, women from the combined interventions arm were more likely to have received timely treatment from a skilled provider (61.6% vs. 47.2%; p = 0.028). A community-based intervention combining the supportive supervision of community health workers with intensive community mobilization and can be effective in improving care-seeking and preventive behaviour and may be used to strengthen the national malaria control programme.

Reliable numerical computation in an optimal output-feedback design

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Data-driven gradient algorithm for high-precision quantum control

NASA Astrophysics Data System (ADS)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Control Improvement for Jump-Diffusion Processes with Applications to Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baeuerle, Nicole, E-mail: nicole.baeuerle@kit.edu; Rieder, Ulrich, E-mail: ulrich.rieder@uni-ulm.de

2012-02-15

We consider stochastic control problems with jump-diffusion processes and formulate an algorithm which produces, starting from a given admissible control {pi}, a new control with a better value. If no improvement is possible, then {pi} is optimal. Such an algorithm is well-known for discrete-time Markov Decision Problems under the name Howard's policy improvement algorithm. The idea can be traced back to Bellman. Here we show with the help of martingale techniques that such an algorithm can also be formulated for stochastic control problems with jump-diffusion processes. As an application we derive some interesting results in financial portfolio optimization.

Load Frequency Control of AC Microgrid Interconnected Thermal Power System

NASA Astrophysics Data System (ADS)

Lal, Deepak Kumar; Barisal, Ajit Kumar

2017-08-01

In this paper, a microgrid (MG) power generation system is interconnected with a single area reheat thermal power system for load frequency control study. A new meta-heuristic optimization algorithm i.e. Moth-Flame Optimization (MFO) algorithm is applied to evaluate optimal gains of the fuzzy based proportional, integral and derivative (PID) controllers. The system dynamic performance is studied by comparing the results with MFO optimized classical PI/PID controllers. Also the system performance is investigated with fuzzy PID controller optimized by recently developed grey wolf optimizer (GWO) algorithm, which has proven its superiority over other previously developed algorithm in many interconnected power systems.

Approximation algorithms for planning and control

NASA Technical Reports Server (NTRS)

Boddy, Mark; Dean, Thomas

1989-01-01

A control system operating in a complex environment will encounter a variety of different situations, with varying amounts of time available to respond to critical events. Ideally, such a control system will do the best possible with the time available. In other words, its responses should approximate those that would result from having unlimited time for computation, where the degree of the approximation depends on the amount of time it actually has. There exist approximation algorithms for a wide variety of problems. Unfortunately, the solution to any reasonably complex control problem will require solving several computationally intensive problems. Algorithms for successive approximation are a subclass of the class of anytime algorithms, algorithms that return answers for any amount of computation time, where the answers improve as more time is allotted. An architecture is described for allocating computation time to a set of anytime algorithms, based on expectations regarding the value of the answers they return. The architecture described is quite general, producing optimal schedules for a set of algorithms under widely varying conditions.

Health care information seeking and seniors: determinants of Internet use.

PubMed

Sheng, Xiaojing; Simpson, Penny M

2015-01-01

While seniors are the most likely population segment to have chronic diseases, they are the least likely to seek information about health and diseases on the Internet. An understanding of factors that impact seniors' usage of the Internet for health care information may provide them with tools needed to improve health. This research examined some of these factors as identified in the comprehensive model of information seeking to find that demographics, trust in health information websites, perceived usefulness of the Internet, and internal locus of control each significantly impact seniors' use of the Internet to seek health information.

Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms

PubMed Central

Van Herpe, Tom; De Brabanter, Jos; Beullens, Martine; De Moor, Bart; Van den Berghe, Greet

2008-01-01

Introduction Blood glucose (BG) control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New (semi-automated) 'BG control' algorithms (or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed. Methods Two ICU patient populations (with different baseline characteristics) were studied, both treated with a similar 'nurse-driven' insulin titration algorithm targeting BG levels of 80 to 110 mg/dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters (the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis. Results The glycemic penalty index (GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 (no penalty) and 100 (the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A higher BG sampling frequency and a longer algorithm application duration resulted in an apparently better performance, as indicated by a lower GPI. Conclusion The GPI is an alternative method for evaluating the performance of BG control algorithms. The blood glucose sampling frequency and the duration of algorithm application should be similar when comparing algorithms. PMID:18302732

Modeling of low-finesse, extrinsic fiber optic Fabry-Perot white light interferometers

NASA Astrophysics Data System (ADS)

Ma, Cheng; Tian, Zhipeng; Wang, Anbo

2012-06-01

This article introduces an approach for modeling the fiber optic low-finesse extrinsic Fabry-Pérot Interferometers (EFPI), aiming to address signal processing problems in EFPI demodulation algorithms based on white light interferometry. The main goal is to seek physical interpretations to correlate the sensor spectrum with the interferometer geometry (most importantly, the optical path difference). Because the signal demodulation quality and reliability hinge heavily on the understanding of such relationships, the model sheds light on optimizing the sensor performance.

SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

NASA Astrophysics Data System (ADS)

Shams, Bita; Haratizadeh, Saman

2016-09-01

Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.

Health Seeking Behaviour among Tuberculosis Patients in India: A Systematic Review

PubMed Central

2016-01-01

Introduction The Revised National Tuberculosis Control Programme’s (RNTCP) passive case finding approach strongly influence the health seeking behaviour of patients and the timing of health seeking as well. Aim A systematic review was carried out to understand the health seeking behaviour, related delays and the knowledge and attitude regarding Tuberculosis (TB) and the health services linked with it. Materials and Methods A manual search strategy was adopted using PUBMED and Google Scholar search engines to obtain research papers in the said subject. Of 113 articles obtained by the end of this search process 10 full text articles were finally selected for the purpose of this review. Results Of the 10 studies identified, the results were delineated in 7 thematic areas such as: (1) Knowledge and perception of TB patients regarding TB and health services for TB; (2) Delays in seeking help; (3) Facility based health seeking behaviour; (4) Reasons for not seeking care/Delay in seeking care; (5) Geographical pattern (Rural-Urban) of health seeking; (6) Socio-cultural factors associated with health seeking; and (7) Gender based health seeking behaviour. Conclusion Health seeking behaviour and related delays are of utmost importance in TB care from two important perspectives; firstly TB requires timely treatment and secondly it requires protracted treatment. Required level of knowledge and positive health behaviour helps the patients in taking timely help from appropriate health facility. PMID:27891362

Health Seeking Behaviour among Tuberculosis Patients in India: A Systematic Review.

PubMed

Samal, Janmejaya

2016-10-01

The Revised National Tuberculosis Control Programme's (RNTCP) passive case finding approach strongly influence the health seeking behaviour of patients and the timing of health seeking as well. A systematic review was carried out to understand the health seeking behaviour, related delays and the knowledge and attitude regarding Tuberculosis (TB) and the health services linked with it. A manual search strategy was adopted using PUBMED and Google Scholar search engines to obtain research papers in the said subject. Of 113 articles obtained by the end of this search process 10 full text articles were finally selected for the purpose of this review. Of the 10 studies identified, the results were delineated in 7 thematic areas such as: (1) Knowledge and perception of TB patients regarding TB and health services for TB; (2) Delays in seeking help; (3) Facility based health seeking behaviour; (4) Reasons for not seeking care/Delay in seeking care; (5) Geographical pattern (Rural-Urban) of health seeking; (6) Socio-cultural factors associated with health seeking; and (7) Gender based health seeking behaviour. Health seeking behaviour and related delays are of utmost importance in TB care from two important perspectives; firstly TB requires timely treatment and secondly it requires protracted treatment. Required level of knowledge and positive health behaviour helps the patients in taking timely help from appropriate health facility.

Genetic algorithms for adaptive real-time control in space systems

NASA Technical Reports Server (NTRS)

Vanderzijp, J.; Choudry, A.

1988-01-01

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

An observer-based compensator for distributed delays

NASA Technical Reports Server (NTRS)

Luck, Rogelio; Ray, Asok

1990-01-01

This paper presents an algorithm for compensating delays that are distributed between the sensor(s), controller and actuator(s) within a control loop. This observer-based algorithm is specially suited to compensation of network-induced delays in integrated communication and control systems. The robustness of the algorithm relative to plant model uncertainties has been examined.

Robust Control Systems.

DTIC Science & Technology

1981-12-01

time control system algorithms that will perform adequately (i.e., at least maintain closed-loop system stability) when ucertain parameters in the...system design models vary significantly. Such a control algorithm is said to have stability robustness-or more simply is said to be "robust". This...cas6s above, the performance is analyzed using a covariance analysis. The development of all the controllers and the performance analysis algorithms is

Grey Wolf based control for speed ripple reduction at low speed operation of PMSM drives.

PubMed

Djerioui, Ali; Houari, Azeddine; Ait-Ahmed, Mourad; Benkhoris, Mohamed-Fouad; Chouder, Aissa; Machmoum, Mohamed

2018-03-01

Speed ripple at low speed-high torque operation of Permanent Magnet Synchronous Machine (PMSM) drives is considered as one of the major issues to be treated. The presented work proposes an efficient PMSM speed controller based on Grey Wolf (GW) algorithm to ensure a high-performance control for speed ripple reduction at low speed operation. The main idea of the proposed control algorithm is to propose a specific objective function in order to incorporate the advantage of fast optimization process of the GW optimizer. The role of GW optimizer is to find the optimal input controls that satisfy the speed tracking requirements. The synthesis methodology of the proposed control algorithm is detailed and the feasibility and performances of the proposed speed controller is confirmed by simulation and experimental results. The GW algorithm is a model-free controller and the parameters of its objective function are easy to be tuned. The GW controller is compared to PI one on real test bench. Then, the superiority of the first algorithm is highlighted. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Strategic Control Algorithm Development : Volume 4A. Computer Program Report.

DOT National Transportation Integrated Search

1974-08-01

A description of the strategic algorithm evaluation model is presented, both at the user and programmer levels. The model representation of an airport configuration, environmental considerations, the strategic control algorithm logic, and the airplan...

Strategic Control Algorithm Development : Volume 4B. Computer Program Report (Concluded)

DOT National Transportation Integrated Search

1974-08-01

A description of the strategic algorithm evaluation model is presented, both at the user and programmer levels. The model representation of an airport configuration, environmental considerations, the strategic control algorithm logic, and the airplan...

Learning algorithms for human-machine interfaces.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2009-05-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore-Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction.

Learning Algorithms for Human–Machine Interfaces

PubMed Central

Fishbach, Alon; Mussa-Ivaldi, Ferdinando A.

2012-01-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore–Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction. PMID:19203886

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Pre-Hardware Optimization of Spacecraft Image Processing Algorithms and Hardware Implementation

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Petrick, David J.; Flatley, Thomas P.; Hestnes, Phyllis; Jentoft-Nilsen, Marit; Day, John H. (Technical Monitor)

2002-01-01

Spacecraft telemetry rates and telemetry product complexity have steadily increased over the last decade presenting a problem for real-time processing by ground facilities. This paper proposes a solution to a related problem for the Geostationary Operational Environmental Spacecraft (GOES-8) image data processing and color picture generation application. Although large super-computer facilities are the obvious heritage solution, they are very costly, making it imperative to seek a feasible alternative engineering solution at a fraction of the cost. The proposed solution is based on a Personal Computer (PC) platform and synergy of optimized software algorithms, and reconfigurable computing hardware (RC) technologies, such as Field Programmable Gate Arrays (FPGA) and Digital Signal Processors (DSP). It has been shown that this approach can provide superior inexpensive performance for a chosen application on the ground station or on-board a spacecraft.

Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

NASA Astrophysics Data System (ADS)

Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin

2010-08-01

In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.

Quantum Parameter Estimation: From Experimental Design to Constructive Algorithm

NASA Astrophysics Data System (ADS)

Yang, Le; Chen, Xi; Zhang, Ming; Dai, Hong-Yi

2017-11-01

In this paper we design the following two-step scheme to estimate the model parameter ω 0 of the quantum system: first we utilize the Fisher information with respect to an intermediate variable v=\\cos ({ω }0t) to determine an optimal initial state and to seek optimal parameters of the POVM measurement operators; second we explore how to estimate ω 0 from v by choosing t when a priori information knowledge of ω 0 is available. Our optimal initial state can achieve the maximum quantum Fisher information. The formulation of the optimal time t is obtained and the complete algorithm for parameter estimation is presented. We further explore how the lower bound of the estimation deviation depends on the a priori information of the model. Supported by the National Natural Science Foundation of China under Grant Nos. 61273202, 61673389, and 61134008

The impact on anxiety and perceived control of a short one-on-one nursing intervention designed to decrease treatment seeking delay in people with coronary heart disease.

PubMed

Moser, Debra K; McKinley, Sharon; Riegel, Barbara; Doering, Lynn V; Meischke, Hendrika; Pelter, Michele; Davidson, Patricia; Baker, Heather; Dracup, Kathleen

2012-06-01

Patient delay in seeking treatment for acute coronary syndrome symptoms remains a problem. Thus, it is vital to test interventions to improve this behavior, but at the same time it is essential that interventions not increase anxiety. To determine the impact on anxiety and perceived control of an individual face-to-face education and counseling intervention designed to decrease patient delay in seeking treatment for acute coronary syndrome symptoms. This was a multicenter randomized controlled trial of the intervention in which anxiety data were collected at baseline, 3-months and 12-months. A total of 3522 patients with confirmed coronary artery disease were enrolled; data from 2597 patients with anxiety data at all time points are included. The intervention was a 45 min education and counseling session, in which the social, cognitive and emotional responses to acute coronary syndrome symptoms were discussed as were barriers to early treatment seeking. Repeated measures analysis of covariance was used to compare anxiety and perceived control levels across time between the groups controlling for age, gender, ethnicity, education level, and comorbidities. There were significant differences in anxiety by group (p = 0.03). Anxiety level was stable in patients in the control group, but decreased across time in the intervention group. Perceived control increased across time in the intervention group and remained unchanged in the control group (p = 0.01). Interventions in which cardiac patients directly confront the possibility of an acute cardiac event do not cause anxiety if they provide patients with appropriate strategies for managing symptoms.

Drug information-seeking intention and behavior after exposure to direct-to-consumer advertisement of prescription drugs.

PubMed

Liu, Yifei; Doucette, William R; Farris, Karen B; Nayakankuppam, Dhananjay

2005-06-01

Concerns about direct-to-consumer advertisement's (DTCA's) information quality have raised interest in patients' drug information-seeking after DTCA exposure. To identify predictors of patients' intentions and behaviors to seek drug information from physicians, pharmacists, and the Internet after DTCA exposure, using theories of planned behavior and self-efficacy. One thousand patients were randomly selected from 3,000 nationwide osteoarthritic patients. A self-administered survey examined predictors of intention including measurements of attitude toward behavior, subjective norm, perceived difficulty, self-efficacy, controllability, self-identity, intention, exposure to ads, and control variables. After 6 weeks, another survey measured respondents' information-seeking behavior. For patients exposed to DTCA, 6 multiple regressions were performed for information-seeking intention and behavior for 3 information sources: physicians, pharmacists, and the Internet. The response rates were 61.9% and 80.1% for the first survey and the second survey, respectively. Four hundred and fifty-four participants reported exposure to DTCA about arthritis prescription medicines in the previous month. Over 41% of the variance in intention and over 18% of the variance in behavior were explained by the regression procedures. The consistent positive predictors of intention were attitude toward behavior, self-identity, attitude toward DTCAs of arthritis medication, and osteoarthritis pain; while the consistent positive predictors of behavior were intention and osteoarthritis pain. The strongest predictors of intention were self-identity for physicians, subjective norm for pharmacists, and attitude toward behavior for the Internet. Perceived difficulty and self-efficacy did not predict intention, and self-efficacy and controllability did not predict behavior. DTCA-prompted drug information-seeking may be under patients' complete volitional control. To promote information searching, efforts could be made to affect factors predicting intention. Interventions could address patients' attitude toward behavior, the influence of their important others, and their role as information seeker, respectively, for information sources like the Internet, pharmacists, and physicians.

Fuzzy PID control algorithm based on PSO and application in BLDC motor

NASA Astrophysics Data System (ADS)

Lin, Sen; Wang, Guanglong

2017-06-01

A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

Resource Balancing Control Allocation

NASA Technical Reports Server (NTRS)

Frost, Susan A.; Bodson, Marc

2010-01-01

Next generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the l1 or l2 norms of the tracking error and of the control effort. The paper discusses the alternative choice of using the l1 norm for minimization of the tracking error and a normalized l(infinity) norm, or sup norm, for minimization of the control effort. The algorithm computes the norm of the actuator deflections scaled by the actuator limits. Minimization of the control effort then translates into the minimization of the maximum actuator deflection as a percentage of its range of motion. The paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are investigated through examples. In particular, the min-max criterion results in a type of resource balancing, where the resources are the control surfaces and the algorithm balances these resources to achieve the desired command. A study of the sensitivity of the algorithms to the data is presented, which shows that the normalized l(infinity) algorithm has the lowest sensitivity, although high sensitivities are observed whenever the limits of performance are reached.

Development of Algorithms for Control of Humidity in Plant Growth Chambers

NASA Technical Reports Server (NTRS)

Costello, Thomas A.

2003-01-01

Algorithms were developed to control humidity in plant growth chambers used for research on bioregenerative life support at Kennedy Space Center. The algorithms used the computed water vapor pressure (based on measured air temperature and relative humidity) as the process variable, with time-proportioned outputs to operate the humidifier and de-humidifier. Algorithms were based upon proportional-integral-differential (PID) and Fuzzy Logic schemes and were implemented using I/O Control software (OPTO-22) to define and download the control logic to an autonomous programmable logic controller (PLC, ultimate ethernet brain and assorted input-output modules, OPTO-22), which performed the monitoring and control logic processing, as well the physical control of the devices that effected the targeted environment in the chamber. During limited testing, the PLC's successfully implemented the intended control schemes and attained a control resolution for humidity of less than 1%. The algorithms have potential to be used not only with autonomous PLC's but could also be implemented within network-based supervisory control programs. This report documents unique control features that were implemented within the OPTO-22 framework and makes recommendations regarding future uses of the hardware and software for biological research by NASA.

Operational space trajectory tracking control of robot manipulators endowed with a primary controller of synthetic joint velocity.

PubMed

Moreno-Valenzuela, Javier; González-Hernández, Luis

2011-01-01

In this paper, a new control algorithm for operational space trajectory tracking control of robot arms is introduced. The new algorithm does not require velocity measurement and is based on (1) a primary controller which incorporates an algorithm to obtain synthesized velocity from joint position measurements and (2) a secondary controller which computes the desired joint acceleration and velocity required to achieve operational space motion control. The theory of singularly perturbed systems is crucial for the analysis of the closed-loop system trajectories. In addition, the practical viability of the proposed algorithm is explored through real-time experiments in a two degrees-of-freedom horizontal planar direct-drive arm. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Resource sharing on CSMA/CD networks in the presence of noise. M.S. Thesis

NASA Technical Reports Server (NTRS)

Dinschel, Duane Edward

1987-01-01

Resource sharing on carrier sense multiple access with collision detection (CSMA/CD) networks can be accomplished by using window-control algorithms for bus contention. The window-control algorithms are designed to grant permission to transmit to the station with the minimum contention parameter. Proper operation of the window-control algorithm requires that all stations sense the same state of the newtork in each contention slot. Noise causes the state of the network to appear as a collision. False collisions can cause the window-control algorithm to terminate without isolating any stations. A two-phase window-control protocol and approximate recurrence equation with noise as a parameter to improve the performance of the window-control algorithms in the presence of noise are developed. The results are compared through simulation, with the approximate recurrence equation yielding the best overall performance. Noise is even a bigger problem when it is not detected by all stations. In such cases it is possible for the window boundaries of the contending stations to become out of phase. Consequently, it is possible to isolate a station other than the one with the minimum contention parameter. To guarantee proper isolation of the minimum, a broadcast phase must be added after the termination of the algorithm. The protocol required to correct the window-control algorithm when noise is not detected by all stations is discussed.

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Carson, John M., III

2007-01-01

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

Extinction of cue-evoked drug-seeking relies on degrading hierarchical instrumental expectancies

PubMed Central

Hogarth, Lee; Retzler, Chris; Munafò, Marcus R.; Tran, Dominic M.D.; Troisi, Joseph R.; Rose, Abigail K.; Jones, Andrew; Field, Matt

2014-01-01

There has long been need for a behavioural intervention that attenuates cue-evoked drug-seeking, but the optimal method remains obscure. To address this, we report three approaches to extinguish cue-evoked drug-seeking measured in a Pavlovian to instrumental transfer design, in non-treatment seeking adult smokers and alcohol drinkers. The results showed that the ability of a drug stimulus to transfer control over a separately trained drug-seeking response was not affected by the stimulus undergoing Pavlovian extinction training in experiment 1, but was abolished by the stimulus undergoing discriminative extinction training in experiment 2, and was abolished by explicit verbal instructions stating that the stimulus did not signal a more effective response-drug contingency in experiment 3. These data suggest that cue-evoked drug-seeking is mediated by a propositional hierarchical instrumental expectancy that the drug-seeking response is more likely to be rewarded in that stimulus. Methods which degraded this hierarchical expectancy were effective in the laboratory, and so may have therapeutic potential. PMID:25011113

Peak-Seeking Optimization of Spanwise Lift Distribution for Wings in Formation Flight

NASA Technical Reports Server (NTRS)

Hanson, Curtis E.; Ryan, Jack

2012-01-01

A method is presented for the in-flight optimization of the lift distribution across the wing for minimum drag of an aircraft in formation flight. The usual elliptical distribution that is optimal for a given wing with a given span is no longer optimal for the trailing wing in a formation due to the asymmetric nature of the encountered flow field. Control surfaces along the trailing edge of the wing can be configured to obtain a non-elliptical profile that is more optimal in terms of minimum combined induced and profile drag. Due to the difficult-to-predict nature of formation flight aerodynamics, a Newton-Raphson peak-seeking controller is used to identify in real time the best aileron and flap deployment scheme for minimum total drag. Simulation results show that the peak-seeking controller correctly identifies an optimal trim configuration that provides additional drag savings above those achieved with conventional anti-symmetric aileron trim.

Effects of sublethal exposure to boric acid sugar bait on adult survival, host-seeking, bloodfeeding behavior, and reproduction of Stegomyia albopicta.

PubMed

Ali, Arshad; Xue, Rui-De; Barnard, Donald R

2006-09-01

Effects of sublethal exposure to 0.1% boric acid sugar bait on adult survival, host-seeking, bloodfeeding behavior, and reproduction of Stegomyia albopicta were studied in the laboratory. Survival of males as well as females was significantly reduced when exposed to the bait, compared to control adults. The host-seeking and bloodfeeding activities in the baited females decreased, but the mean duration of blood engorgement (probing to voluntary withdrawal of proboscis) was not significantly different between the baited and control females. The landing and biting rates (human forearm) were significantly reduced in the baited females compared to nonbaited controls. Fecundity and fertility (based on number of laid eggs per female and percentage egg hatch, respectively) in the baited females were significantly reduced, and ovarian development was retarded. Sublethal exposure to sugar-based boric acid bait has the potential to reduce adult populations of St. albopicta.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is suited for nonlinear optimization problems. The parallel computation of the algorithm exploits iterative linear algebra methods for the main linear algebra computations in the algorithm. We show that the splitting of the algorithm is flexible and can thus be applied to various distributed platform configurations. The two proposed algorithms are applied to two main energy and transportation control problems. The first application is energy efficient building control. Buildings represent 40% of energy consumption in the United States. Thus, it is significant to improve the energy efficiency of buildings. The goal is to minimize energy consumption subject to the physics of the building (e.g. heat transfer laws), the constraints of the actuators as well as the desired operating constraints (thermal comfort of the occupants), and heat load on the system. In this thesis, we describe the control systems of forced air building systems in practice. We discuss the "Trim and Respond" algorithm which is a distributed control algorithm that is used in practice, and show that it performs similarly to a one-step explicit DMPC algorithm. Then, we apply the novel distributed primal-dual active-set method and provide extensive numerical results for the building MPC problem. The second main application is the control of ramp metering signals to optimize traffic flow through a freeway system. This application is particularly important since urban congestion has more than doubled in the past few decades. The ramp metering problem is to maximize freeway throughput subject to freeway dynamics (derived from mass conservation), actuation constraints, freeway capacity constraints, and predicted traffic demand. In this thesis, we develop a hybrid model predictive controller for ramp metering that is guaranteed to be persistently feasible and stable. This contrasts to previous work on MPC for ramp metering where such guarantees are absent. We apply a smoothing method to the hybrid model predictive controller and apply the inexact interior point method to this nonlinear non-convex ramp metering problem.

Interaction between the basolateral amygdala and dorsal hippocampus is critical for cocaine memory reconsolidation and subsequent drug context-induced cocaine-seeking behavior in rats

PubMed Central

Wells, Audrey M.; Lasseter, Heather C.; Xie, Xiaohu; Cowhey, Kate E.; Reittinger, Andrew M.; Fuchs, Rita A.

2011-01-01

Contextual stimulus control over instrumental drug-seeking behavior relies on the reconsolidation of context-response-drug associative memories into long-term memory storage following retrieval-induced destabilization. According to previous studies, the basolateral amygdala (BLA) and dorsal hippocampus (DH) regulate cocaine-related memory reconsolidation; however, it is not known whether these brain regions interact or independently control this phenomenon. To investigate this question, rats were trained to lever press for cocaine reinforcement in a distinct environmental context followed by extinction training in a different context. Rats were then briefly re-exposed to the cocaine-paired context to destabilize cocaine-related memories, or they were exposed to an unpaired context. Immediately thereafter, the rats received unilateral microinfusions of anisomycin (ANI) into the BLA plus baclofen/muscimol (B/M) into the contralateral (BLA/DH disconnection) or ipsilateral DH, or they received contralateral or ipsilateral microinfusions of vehicle. They then remained in their home cages overnight or for 21 d, followed by additional extinction training and a test of cocaine-seeking behavior (nonreinforced active lever responding). BLA/DH disconnection following re-exposure to the cocaine-paired context, but not the unpaired context, impaired subsequent drug context-induced cocaine-seeking behavior relative to vehicle or ipsilateral ANI + B/M treatment. Prolonged home cage stay elicited a time-dependent increase, or incubation, of drug-context-induced cocaine-seeking behavior, and BLA/DH disconnection inhibited this incubation effect despite some recovery of cocaine-seeking behavior. Thus, the BLA and DH interact to regulate the reconsolidation of cocaine-related associative memories, thereby facilitating the ability of drug-paired contexts to trigger cocaine-seeking behavior and contributing to the incubation of cocaine-seeking behavior. PMID:22005750

Plasma cortisol and oxytocin levels predict help-seeking intentions for depressive symptoms.

PubMed

Thomas, Susan; Larkin, Theresa

2018-01-01

Depressed individuals often refuse or withdraw from help, a phenomenon termed help-negation, which is a risk factor for poor outcomes. Most previous research has investigated psychosocial factors including stigma as causes of low help-seeking intentions for depression, however these do not adequately explain the problem. We hypothesised that because help-negation worsens with symptom severity, it might be linked to important biological changes associated with depression itself. We investigated the relative contributions of cortisol, a stress hormone linked to depression, and oxytocin, a hormone which mediates social behaviours, alongside psychosocial factors, to help-seeking intentions among depressed and non-depressed individuals. Morning plasma cortisol and oxytocin levels, psychopathology, suicidal ideation, help-seeking intentions from informal sources including family and friends, and formal sources including health professionals, and perceived social support were quantified in 63 adults meeting DSM-5 criteria for major depressive disorder (MDD) who were not receiving any treatment, and 60 healthy controls. Between-group analyses of variance, correlations, and hierarchical multiple regressions were employed. Help-seeking intentions were lower in depressed than healthy participants, negatively correlated to cortisol and positively correlated to oxytocin. Cortisol negatively, and oxytocin positively, predicted help-seeking intentions from informal but not formal sources, after controlling for psychopathology and psychosocial factors. Neuroendocrine changes associated with depression may contribute to low help-seeking from friends and family, which may have implications for interpersonal support and outcomes. Research and clinical approaches which incorporate biological as well as psychosocial factors may allow for more targeted and effective early interventions to address lack of help-seeking and depression progression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating Contrail Climate Effects from Satellite Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Duda, David P.; Palikonda, Rabindra; Bedka, Sarah T.; Boeke, Robyn; Khlopenkov, Konstantin; Chee, Thad; Bedka, Kristopher T.

2011-01-01

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate.

Left ventricle segmentation via graph cut distribution matching.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Li, Shuo; Islam, Ali; Chong, Jaron

2009-01-01

We present a discrete kernel density matching energy for segmenting the left ventricle cavity in cardiac magnetic resonance sequences. The energy and its graph cut optimization based on an original first-order approximation of the Bhattacharyya measure have not been proposed previously, and yield competitive results in nearly real-time. The algorithm seeks a region within each frame by optimization of two priors, one geometric (distance-based) and the other photometric, each measuring a distribution similarity between the region and a model learned from the first frame. Based on global rather than pixelwise information, the proposed algorithm does not require complex training and optimization with respect to geometric transformations. Unlike related active contour methods, it does not compute iterative updates of computationally expensive kernel densities. Furthermore, the proposed first-order analysis can be used for other intractable energies and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of graph cuts. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert.

Nemesis Autonomous Test System

NASA Technical Reports Server (NTRS)

Barltrop, Kevin J.; Lee, Cin-Young; Horvath, Gregory A,; Clement, Bradley J.

2012-01-01

A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.

A Battery-Aware Algorithm for Supporting Collaborative Applications

NASA Astrophysics Data System (ADS)

Rollins, Sami; Chang-Yit, Cheryl

Battery-powered devices such as laptops, cell phones, and MP3 players are becoming ubiquitous. There are several significant ways in which the ubiquity of battery-powered technology impacts the field of collaborative computing. First, applications such as collaborative data gathering, become possible. Also, existing applications that depend on collaborating devices to maintain the system infrastructure must be reconsidered. Fundamentally, the problem lies in the fact that collaborative applications often require end-user computing devices to perform tasks that happen in the background and are not directly advantageous to the user. In this work, we seek to better understand how laptop users use the batteries attached to their devices and analyze a battery-aware alternative to Gnutella’s ultrapeer selection algorithm. Our algorithm provides insight into how system maintenance tasks can be allocated to battery-powered nodes. The most significant result of our study indicates that a large portion of laptop users can participate in system maintenance without sacrificing any of their battery. These results show great promise for existing collaborative applications as well as new applications, such as collaborative data gathering, that rely upon battery-powered devices.

Social network fragmentation and community health.

PubMed

Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

2017-09-05

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

Gradient Optimization for Analytic conTrols - GOAT

NASA Astrophysics Data System (ADS)

Assémat, Elie; Machnes, Shai; Tannor, David; Wilhelm-Mauch, Frank

Quantum optimal control becomes a necessary step in a number of studies in the quantum realm. Recent experimental advances showed that superconducting qubits can be controlled with an impressive accuracy. However, most of the standard optimal control algorithms are not designed to manage such high accuracy. To tackle this issue, a novel quantum optimal control algorithm have been introduced: the Gradient Optimization for Analytic conTrols (GOAT). It avoids the piecewise constant approximation of the control pulse used by standard algorithms. This allows an efficient implementation of very high accuracy optimization. It also includes a novel method to compute the gradient that provides many advantages, e.g. the absence of backpropagation or the natural route to optimize the robustness of the control pulses. This talk will present the GOAT algorithm and a few applications to transmons systems.

Large space structures control algorithm characterization

NASA Technical Reports Server (NTRS)

Fogel, E.

1983-01-01

Feedback control algorithms are developed for sensor/actuator pairs on large space systems. These algorithms have been sized in terms of (1) floating point operation (FLOP) demands; (2) storage for variables; and (3) input/output data flow. FLOP sizing (per control cycle) was done as a function of the number of control states and the number of sensor/actuator pairs. Storage for variables and I/O sizing was done for specific structure examples.

Power efficient control algorithm of electromechanical unbalance vibration exciter with induction motor

NASA Astrophysics Data System (ADS)

Topovskiy, V. V.; Simakov, G. M.

2017-10-01

A control algorithm of an electromechanical unbalance vibration exciter that provides a free rotational movement is offered in the paper. The unbalance vibration exciter control system realizing a free rotational movement has been synthesized. The structured modeling of the synthesized system has been carried out and its transients are presented. The advantages and disadvantages of the proposed control algorithm applied to the unbalance vibration exciter are shown.

Spacecraft Alignment Determination and Control for Dual Spacecraft Precision Formation Flying

NASA Technical Reports Server (NTRS)

Calhoun, Philip; Novo-Gradac, Anne-Marie; Shah, Neerav

2017-01-01

Many proposed formation flying missions seek to advance the state of the art in spacecraft science imaging by utilizing precision dual spacecraft formation flying to enable a virtual space telescope. Using precision dual spacecraft alignment, very long focal lengths can be achieved by locating the optics on one spacecraft and the detector on the other. Proposed science missions include astrophysics concepts with spacecraft separations from 1000 km to 25,000 km, such as the Milli-Arc-Second Structure Imager (MASSIM) and the New Worlds Observer, and Heliophysics concepts for solar coronagraphs and X-ray imaging with smaller separations (50m-500m). All of these proposed missions require advances in guidance, navigation, and control (GNC) for precision formation flying. In particular, very precise astrometric alignment control and estimation is required for precise inertial pointing of the virtual space telescope to enable science imaging orders of magnitude better than can be achieved with conventional single spacecraft instruments. This work develops design architectures, algorithms, and performance analysis of proposed GNC systems for precision dual spacecraft astrometric alignment. These systems employ a variety of GNC sensors and actuators, including laser-based alignment and ranging systems, optical imaging sensors (e.g. guide star telescope), inertial measurement units (IMU), as well as microthruster and precision stabilized platforms. A comprehensive GNC performance analysis is given for Heliophysics dual spacecraft PFF imaging mission concept.

Spacecraft Alignment Determination and Control for Dual Spacecraft Precision Formation Flying

NASA Technical Reports Server (NTRS)

Calhoun, Philip C.; Novo-Gradac, Anne-Marie; Shah, Neerav

2017-01-01

Many proposed formation flying missions seek to advance the state of the art in spacecraft science imaging by utilizing precision dual spacecraft formation flying to enable a virtual space telescope. Using precision dual spacecraft alignment, very long focal lengths can be achieved by locating the optics on one spacecraft and the detector on the other. Proposed science missions include astrophysics concepts with spacecraft separations from 1000 km to 25,000 km, such as the Milli-Arc-Second Structure Imager (MASSIM) and the New Worlds Observer, and Heliophysics concepts for solar coronagraphs and X-ray imaging with smaller separations (50m 500m). All of these proposed missions require advances in guidance, navigation, and control (GNC) for precision formation flying. In particular, very precise astrometric alignment control and estimation is required for precise inertial pointing of the virtual space telescope to enable science imaging orders of magnitude better than can be achieved with conventional single spacecraft instruments. This work develops design architectures, algorithms, and performance analysis of proposed GNC systems for precision dual spacecraft astrometric alignment. These systems employ a variety of GNC sensors and actuators, including laser-based alignment and ranging systems, optical imaging sensors (e.g. guide star telescope), inertial measurement units (IMU), as well as micro-thruster and precision stabilized platforms. A comprehensive GNC performance analysis is given for Heliophysics dual spacecraft PFF imaging mission concept.

Finite Set Control Transcription for Optimal Control Applications

DTIC Science & Technology

2009-05-01

Figures 1.1 The Parameters of x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1 Categories of Optimization Algorithms ...Programming (NLP) algorithm , such as SNOPT2 (hereafter, called the optimizer). The Finite Set Control Transcription (FSCT) method is essentially a...artificial neural networks, ge- netic algorithms , or combinations thereof for analysis.4,5 Indeed, an actual biological neural network is an example of

A semi-active suspension control algorithm for vehicle comprehensive vertical dynamics performance

NASA Astrophysics Data System (ADS)

Nie, Shida; Zhuang, Ye; Liu, Weiping; Chen, Fan

2017-08-01

Comprehensive performance of the vehicle, including ride qualities and road-holding, is essentially of great value in practice. Many up-to-date semi-active control algorithms improve vehicle dynamics performance effectively. However, it is hard to improve comprehensive performance for the conflict between ride qualities and road-holding around the second-order resonance. Hence, a new control algorithm is proposed to achieve a good trade-off between ride qualities and road-holding. In this paper, the properties of the invariant points are analysed, which gives an insight into the performance conflicting around the second-order resonance. Based on it, a new control algorithm is proposed. The algorithm employs a novel frequency selector to balance suspension ride and handling performance by adopting a medium damping around the second-order resonance. The results of this study show that the proposed control algorithm could improve the performance of ride qualities and suspension working space up to 18.3% and 8.2%, respectively, with little loss of road-holding compared to the passive suspension. Consequently, the comprehensive performance can be improved by 6.6%. Hence, the proposed algorithm is of great potential to be implemented in practice.

Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system.

PubMed

Scanlon, John M; Sherony, Rini; Gabler, Hampton C

2016-09-01

Intersection crashes resulted in over 5,000 fatalities in the United States in 2014. Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that seek to help drivers safely traverse intersections. I-ADAS uses onboard sensors to detect oncoming vehicles and, in the event of an imminent crash, can either alert the driver or take autonomous evasive action. The objective of this study was to develop and evaluate a predictive model for detecting whether a stop sign violation was imminent. Passenger vehicle intersection approaches were extracted from a data set of typical driver behavior (100-Car Naturalistic Driving Study) and violations (event data recorders downloaded from real-world crashes) and were assigned weighting factors based on real-world frequency. A k-fold cross-validation procedure was then used to develop and evaluate 3 hypothetical stop sign warning algorithms (i.e., early, intermediate, and delayed) for detecting an impending violation during the intersection approach. Violation detection models were developed using logistic regression models that evaluate likelihood of a violation at various locations along the intersection approach. Two potential indicators of driver intent to stop-that is, required deceleration parameter (RDP) and brake application-were used to develop the predictive models. The earliest violation detection opportunity was then evaluated for each detection algorithm in order to (1) evaluate the violation detection accuracy and (2) compare braking demand versus maximum braking capabilities. A total of 38 violating and 658 nonviolating approaches were used in the analysis. All 3 algorithms were able to detect a violation at some point during the intersection approach. The early detection algorithm, as designed, was able to detect violations earlier than all other algorithms during the intersection approach but gave false alarms for 22.3% of approaches. In contrast, the delayed detection algorithm sacrificed some time for detecting violations but was able to substantially reduce false alarms to only 3.3% of all nonviolating approaches. Given good surface conditions (maximum braking capabilities = 0.8 g) and maximum effort, most drivers (55.3-71.1%) would be able to stop the vehicle regardless of the detection algorithm. However, given poor surface conditions (maximum braking capabilities = 0.4 g), few drivers (10.5-26.3%) would be able to stop the vehicle. Automatic emergency braking (AEB) would allow for early braking prior to driver reaction. If equipped with an AEB system, the results suggest that, even for the poor surface conditions scenario, over one half (55.3-65.8%) of the vehicles could have been stopped. This study demonstrates the potential of I-ADAS to incorporate a stop sign violation detection algorithm. Repeating the analysis on a larger, more extensive data set will allow for the development of a more comprehensive algorithm to further validate the findings.

Reconstructing liver shape and position from MR image slices using an active shape model

NASA Astrophysics Data System (ADS)

Fenchel, Matthias; Thesen, Stefan; Schilling, Andreas

2008-03-01

We present an algorithm for fully automatic reconstruction of 3D position, orientation and shape of the human liver from a sparsely covering set of n 2D MR slice images. Reconstructing the shape of an organ from slice images can be used for scan planning, for surgical planning or other purposes where 3D anatomical knowledge has to be inferred from sparse slices. The algorithm is based on adapting an active shape model of the liver surface to a given set of slice images. The active shape model is created from a training set of liver segmentations from a group of volunteers. The training set is set up with semi-manual segmentations of T1-weighted volumetric MR images. Searching for the optimal shape model that best fits to the image data is done by maximizing a similarity measure based on local appearance at the surface. Two different algorithms for the active shape model search are proposed and compared: both algorithms seek to maximize the a-posteriori probability of the grey level appearance around the surface while constraining the surface to the space of valid shapes. The first algorithm works by using grey value profile statistics in normal direction. The second algorithm uses average and variance images to calculate the local surface appearance on the fly. Both algorithms are validated by fitting the active shape model to abdominal 2D slice images and comparing the shapes, which have been reconstructed, to the manual segmentations and to the results of active shape model searches from 3D image data. The results turn out to be promising and competitive to active shape model segmentations from 3D data.

Coping with stress in adolescents with type 1 diabetes and their mothers.

PubMed

Pisula, Ewa; Czaplinska, C

2010-11-04

Coping with stress plays a vital role in the adjustment of adolescents with diabetes. The majority of studies in this area leave out the control group, limiting their power to make inferences about specificity vs. similarity of coping strategies used by these adolescents. The aims of this study were: (1) To compare coping strategies in adolescents with diabetes and healthy adolescents; (2) To compare coping strategies in girls and boys with diabetes; (3) To determine whether there is a relationship between adolescents' coping strategies and their mothers' coping styles. Adolescents (12-17 years old) with Type 1 diabetes (n = 51) were compared with a control group of healthy secondary school students (n = 56) by means of a self-reported questionnaire measuring coping strategies (Adolescence Coping Checklist). Mothers of these adolescents (n = 107) completed the Coping Inventory for Stressful Situations, measuring 3 coping styles. Diabetic adolescents used the 'seek professional help' strategy more often than their healthy peers. The girls with diabetes reported using the 'investing in close friends' strategy more often than boys, while in the control group girls were also more likely to use "seeking social support", "seeking spiritual support", and "relaxing diversions" strategies. Mothers' emotion-oriented coping style predicted focus-oriented coping in adolescents with diabetes. In the non-diabetic group, mothers' task-oriented coping predicted seeking professional help, while mothers' avoidance-oriented coping predicted seeking spiritual support. The results demonstrated that: (1) the only differences in terms of coping strategies in adolescents with diabetes and healthy adolescents were found in seeking professional help; (2) gender differences in coping with stress were significantly smaller in adolescents with diabetes than in healthy adolescents, (3) mothers' coping styles were predictors of coping strategies in adolescents, albeit there were differences in that respect between adolescents with diabetes and healthy adolescents.

Persistent Pain Maintains Morphine-Seeking Behavior after Morphine Withdrawal through Reduced MeCP2 Repression of Glua1 in Rat Central Amygdala

PubMed Central

Hou, Yuan-Yuan; Cai, You-Qing

2015-01-01

As long-term opioids are increasingly used for control of chronic pain, how pain affects the rewarding effect of opioids and hence risk of prescription opioid misuse and abuse remains a healthcare concern and a challenging issue in current pain management. In this study, using a rat model of morphine self-administration, we investigated the molecular mechanisms underlying the impact of pain on operant behavior of morphine intake and morphine seeking before and after morphine withdrawal. We found that rats with persistent pain consumed a similar amount of daily morphine to that in control rats without pain, but maintained their level-pressing behavior of morphine seeking after abstinence of morphine at 0.2 mg/kg, whereas this behavior was gradually diminished in control rats. In the central nucleus of amygdala (CeA), a limbic structure critically involved in the affective dimension of pain, proteins of GluA1 subunits of glutamate AMPA receptors were upregulated during morphine withdrawal, and viral knockdown of CeA GluA1 eliminated the morphine-seeking behavior in withdrawn rats of the pain group. Chromatin immunoprecipitation analysis revealed that the methyl CpG-binding protein 2 (MeCP2) was enriched in the promoter region of Gria1 encoding GluA1 and this enrichment was significantly attenuated in withdrawn rats of the pain group. Furthermore, viral overexpression of CeA MeCP2 repressed the GluA1 level and eliminated the maintenance of morphine-seeking behavior after morphine withdrawal. These results suggest direct MeCp2 repression of GluA1 function as a likely mechanism for morphine-seeking behavior maintained by long-lasting affective pain after morphine withdrawal. PMID:25716866

Flight Evaluation of an Aircraft with Side and Center Stick Controllers and Rate-Limited Ailerons

NASA Technical Reports Server (NTRS)

Deppe, P. R.; Chalk, C. R.; Shafer, M. F.

1996-01-01

As part of an ongoing government and industry effort to study the flying qualities of aircraft with rate-limited control surface actuators, two studies were previously flown to examine an algorithm developed to reduce the tendency for pilot-induced oscillation when rate limiting occurs. This algorithm, when working properly, greatly improved the performance of the aircraft in the first study. In the second study, however, the algorithm did not initially offer as much improvement. The differences between the two studies caused concern. The study detailed in this paper was performed to determine whether the performance of the algorithm was affected by the characteristics of the cockpit controllers. Time delay and flight control system noise were also briefly evaluated. An in-flight simulator, the Calspan Learjet 25, was programmed with a low roll actuator rate limit, and the algorithm was programmed into the flight control system. Side- and center-stick controllers, force and position command signals, a rate-limited feel system, a low-frequency feel system, and a feel system damper were evaluated. The flight program consisted of four flights and 38 evaluations of test configurations. Performance of the algorithm was determined to be unaffected by using side- or center-stick controllers or force or position command signals. The rate-limited feel system performed as well as the rate-limiting algorithm but was disliked by the pilots. The low-frequency feel system and the feel system damper were ineffective. Time delay and noise were determined to degrade the performance of the algorithm.

Robust tuning of robot control systems

NASA Technical Reports Server (NTRS)

Minis, I.; Uebel, M.

1992-01-01

The computed torque control problem is examined for a robot arm with flexible, geared, joint drive systems which are typical in many industrial robots. The standard computed torque algorithm is not directly applicable to this class of manipulators because of the dynamics introduced by the joint drive system. The proposed approach to computed torque control combines a computed torque algorithm with torque controller at each joint. Three such control schemes are proposed. The first scheme uses the joint torque control system currently implemented on the robot arm and a novel form of the computed torque algorithm. The other two use the standard computed torque algorithm and a novel model following torque control system based on model following techniques. Standard tasks and performance indices are used to evaluate the performance of the controllers. Both numerical simulations and experiments are used in evaluation. The study shows that all three proposed systems lead to improved tracking performance over a conventional PD controller.

Anxiety Diagnoses in Smokers Seeking Cessation Treatment: Relations with Tobacco Dependence, Withdrawal, Outcome, and Response to Treatment

PubMed Central

Piper, Megan E.; Cook, Jessica W.; Schlam, Tanya R.; Jorenby, Douglas E.; Baker, Timothy B.

2010-01-01

Aims To understand the relations amongst anxiety disorders and tobacco dependence, withdrawal symptoms, response to smoking cessation pharmacotherapy, and ability to quit smoking. Design Randomized placebo-controlled clinical trial. Participants received six 10-minute individual counseling sessions and either: placebo, bupropion SR, nicotine patch, nicotine lozenge, bupropion SR+nicotine lozenge, or nicotine patch+nicotine lozenge. Setting Two urban research sites. Participants Data were collected from 1504 daily smokers (>9 cigarettes per day) who were motivated to quit smoking and did not report current diagnoses of schizophrenia or psychosis or bupropion use. Measurements Participants completed baseline assessments, the Composite International Diagnostic Interview and ecological momentary assessments for two weeks. Findings A structured clinical interview identified participants who ever met criteria for a panic attack (n=455), social anxiety (n=199), or generalized anxiety disorder (n=99), and those who qualified for no anxiety diagnosis (n=891). Smokers with anxiety disorders reported higher levels of nicotine dependence and pre-quit withdrawal symptoms. Those ever meeting criteria for panic attacks or social anxiety disorder showed greater quit-day negative affect. Smokers ever meeting criteria for anxiety disorders were less likely to be abstinent at 8-weeks and 6-months postquit and showed no benefit from single-agent or combination-agent pharmacotherapies. Conclusions Anxiety diagnoses were common amongst treatment-seeking smokers and were related to increased motivation to smoke, elevated withdrawal, lack of response to pharmacotherapy, and impaired ability to quit smoking. These findings could guide treatment assignment algorithms and treatment development for smokers with anxiety diagnoses. PMID:20973856

D-Optimal Experimental Design for Contaminant Source Identification

NASA Astrophysics Data System (ADS)

Sai Baba, A. K.; Alexanderian, A.

2016-12-01

Contaminant source identification seeks to estimate the release history of a conservative solute given point concentration measurements at some time after the release. This can be mathematically expressed as an inverse problem, with a linear observation operator or a parameter-to-observation map, which we tackle using a Bayesian approach. Acquisition of experimental data can be laborious and expensive. The goal is to control the experimental parameters - in our case, the sparsity of the sensors, to maximize the information gain subject to some physical or budget constraints. This is known as optimal experimental design (OED). D-optimal experimental design seeks to maximize the expected information gain, and has long been considered the gold standard in the statistics community. Our goal is to develop scalable methods for D-optimal experimental designs involving large-scale PDE constrained problems with high-dimensional parameter fields. A major challenge for the OED, is that a nonlinear optimization algorithm for the D-optimality criterion requires repeated evaluation of objective function and gradient involving the determinant of large and dense matrices - this cost can be prohibitively expensive for applications of interest. We propose novel randomized matrix techniques that bring down the computational costs of the objective function and gradient evaluations by several orders of magnitude compared to the naive approach. The effect of randomized estimators on the accuracy and the convergence of the optimization solver will be discussed. The features and benefits of our new approach will be demonstrated on a challenging model problem from contaminant source identification involving the inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem.

Hedonic orientation moderates the association between cognitive control and affect reactivity to daily hassles in adolescent boys.

PubMed

Klipker, Kathrin; Wrzus, Cornelia; Rauers, Antje; Riediger, Michaela

2017-04-01

People often seek to regulate their affective reactions when confronted with hassles. Hassle reactivity is lower for people with higher cognitive control, presumably because of better affect regulation. Many adolescents, however, show higher hassle reactivity than children, despite better cognitive control. The present study aims to understand whether motivational differences when seeking to regulate affective experiences moderate the association between cognitive control and hassle reactivity in adolescence. We hypothesized that higher cognitive control is related to lower hassle reactivity only for adolescents with a strong hedonic orientation, that is, for adolescents who seek to maintain or enhance positive or to dampen negative affect. We investigated 149 boys' (age range: 10-20 years) hedonic orientation and affect reactivity toward daily hassles during 2 weeks of experience sampling. Higher cognitive control, assessed with a working memory battery in the laboratory, was associated with stronger hassle reactivity in individuals with low hedonic orientation. The more hedonic-oriented individuals were, the lower was their hassle reactivity, but only in combination with high cognitive control. Our findings illustrate that higher cognitive control is not always related to lower hassle reactivity. Rather, when daily hassles compromise affect balance, hedonic orientation is equally important to understand affect reactivity in adolescent boys. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Initial Results of an MDO Method Evaluation Study

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia M.; Kodiyalam, Srinivas

1998-01-01

The NASA Langley MDO method evaluation study seeks to arrive at a set of guidelines for using promising MDO methods by accumulating and analyzing computational data for such methods. The data are collected by conducting a series of re- producible experiments. In the first phase of the study, three MDO methods were implemented in the SIGHT: framework and used to solve a set of ten relatively simple problems. In this paper, we comment on the general considerations for conducting method evaluation studies and report some initial results obtained to date. In particular, although the results are not conclusive because of the small initial test set, other formulations, optimality conditions, and sensitivity of solutions to various perturbations. Optimization algorithms are used to solve a particular MDO formulation. It is then appropriate to speak of local convergence rates and of global convergence properties of an optimization algorithm applied to a specific formulation. An analogous distinction exists in the field of partial differential equations. On the one hand, equations are analyzed in terms of regularity, well-posedness, and the existence and unique- ness of solutions. On the other, one considers numerous algorithms for solving differential equations. The area of MDO methods studies MDO formulations combined with optimization algorithms, although at times the distinction is blurred. It is important to

We introduce an algorithm for the simultaneous reconstruction of faults and slip fields. We prove that the minimum of a related regularized functional converges to the unique solution of the fault inverse problem. We consider a Bayesian approach. We use a parallel multi-core platform and we discuss techniques to save on computational time.

NASA Astrophysics Data System (ADS)

Volkov, D.

2017-12-01

We introduce an algorithm for the simultaneous reconstruction of faults and slip fields on those faults. We define a regularized functional to be minimized for the reconstruction. We prove that the minimum of that functional converges to the unique solution of the related fault inverse problem. Due to inherent uncertainties in measurements, rather than seeking a deterministic solution to the fault inverse problem, we consider a Bayesian approach. The advantage of such an approach is that we obtain a way of quantifying uncertainties as part of our final answer. On the downside, this Bayesian approach leads to a very large computation. To contend with the size of this computation we developed an algorithm for the numerical solution to the stochastic minimization problem which can be easily implemented on a parallel multi-core platform and we discuss techniques to save on computational time. After showing how this algorithm performs on simulated data and assessing the effect of noise, we apply it to measured data. The data was recorded during a slow slip event in Guerrero, Mexico.

Real-time road detection in infrared imagery

NASA Astrophysics Data System (ADS)

Andre, Haritini E.; McCoy, Keith

1990-09-01

Automatic road detection is an important part in many scene recognition applications. The extraction of roads provides a means of navigation and position update for remotely piloted vehicles or autonomous vehicles. Roads supply strong contextual information which can be used to improve the performance of automatic target recognition (ATh) systems by directing the search for targets and adjusting target classification confidences. This paper will describe algorithmic techniques for labeling roads in high-resolution infrared imagery. In addition, realtime implementation of this structural approach using a processor array based on the Martin Marietta Geometric Arithmetic Parallel Processor (GAPPTh) chip will be addressed. The algorithm described is based on the hypothesis that a road consists of pairs of line segments separated by a distance "d" with opposite gradient directions (antiparallel). The general nature of the algorithm, in addition to its parallel implementation in a single instruction, multiple data (SIMD) machine, are improvements to existing work. The algorithm seeks to identify line segments meeting the road hypothesis in a manner that performs well, even when the side of the road is fragmented due to occlusion or intersections. The use of geometrical relationships between line segments is a powerful yet flexible method of road classification which is independent of orientation. In addition, this approach can be used to nominate other types of objects with minor parametric changes.

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

A cluster randomized trial of alcohol prevention in small businesses: a cascade model of help seeking and risk reduction.

PubMed

Reynolds, G Shawn; Bennett, Joel B

2015-01-01

The current study adapted two workplace substance abuse prevention programs and tested a conceptual model of workplace training effects on help seeking and alcohol consumption. Questionnaires were collected 1 month before, 1 month after, and 6 months within a cluster randomized field experiment. Texas small businesses in construction, transportation, and service industries. A total of 1510 employees from 45 businesses were randomly assigned to receive no training or one of the interventions. The interventions were 4-hour on-the-job classroom trainings that encouraged healthy lifestyles and seeking professional help (e.g., from the Employee Assistance Program [EAP]). The Team Awareness Program focused on peer referral and team building. The Choices in Health Promotion Program delivered various health topics based on a needs assessment. Questionnaires measured help-seeking attitudes and behavior, frequency of drinking alcohol, and job-related incidents. Mixed-model repeated-measures analyses of covariance were computed. Relative to the control group, training was associated with significantly greater reductions in drinking frequency, willingness to seek help, and seeking help from the EAP. After including help-seeking attitudes as a covariate, the correlation between training and help seeking becomes nonsignificant. Help-seeking behavior was not correlated with drinking frequency. Training improved help-seeking attitudes and behaviors and decreased alcohol risks. The reductions in drinking alcohol were directly correlated with training and independent from help seeking.

Design of optimised backstepping controller for the synchronisation of chaotic Colpitts oscillator using shark smell algorithm

NASA Astrophysics Data System (ADS)

Fouladi, Ehsan; Mojallali, Hamed

2018-01-01

In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master-slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with those of particle swarm optimisation (PSO) algorithm. Simulation results show better performance in terms of accuracy and convergence for the proposed optimised method compared to PSO optimised controller or any non-optimised backstepping controller.

Effects of escitalopram and imipramine on cocaine reinforcement and drug-seeking behaviors in a rat model of depression.

PubMed

Jastrzębska, Joanna; Frankowska, Małgorzata; Suder, Agata; Wydra, Karolina; Nowak, Ewa; Filip, Małgorzata; Przegaliński, Edmund

2017-10-15

Depression and substance cocaine abuse are disorders with a high frequency of comorbidity. In the present study, we combined bilateral olfactory bulbectomy (OBX), an animal model of depression, with intravenous cocaine self-administration and extinction/reinstatement in rats to investigate the effects of two antidepressant drugs, escitalopram (ESC) and imipramine (IMI), with the goal of determining whether these drugs altered cocaine-induced reinforcement and seeking behaviors. Acute administration of IMI (2.5-30mg/kg) reduced the cocaine reinforcement in OBX and SHAM rats. Moreover, IMI effectively reduced the cocaine-seeking behavior after the drug acute or repeated administration during extinction training in OBX rats and SHAM-operated controls. By contrast, acutely administered ESC (2.5-20mg/kg) did not alter cocaine reinforcement in OBX rats or SHAM-operated controls. The lack of ESC effects was also demonstrated during reinstatement tests to study drug-seeking behavior after ESC repeated daily treatment during extinction trials. However, acute treatment with ESC dose-dependently decreased the cocaine-seeking behavior and relapse triggered by cocaine priming or drug-associated conditioned cues in both OBX and SHAM rats. These results indicate the cocaine anti-reinforcement and anti-seeking efficacy of the two antidepressant drugs studied here. However, the mechanisms for the IMI and ESC activity should be clarified in further studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

PubMed

Wei, Qinglai; Li, Benkai; Song, Ruizhuo

2018-04-01

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

Static vs. dynamic decoding algorithms in a non-invasive body-machine interface

PubMed Central

Seáñez-González, Ismael; Pierella, Camilla; Farshchiansadegh, Ali; Thorp, Elias B.; Abdollahi, Farnaz; Pedersen, Jessica; Mussa-Ivaldi, Ferdinando A.

2017-01-01

In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on 6 subjects with high-level SCI and 8 controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI’s continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use. PMID:28092564

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design

USGS Publications Warehouse

Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter

2012-01-01

Hi‐Desert Water District (HDWD), the primary water‐management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic‐tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive‐use strategy. HDWD wishes to identify the least‐cost conjunctive‐use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed‐integer nonlinear programming (MINLP) groundwater‐management problem seeks to minimize water‐delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater‐level constraints, water‐supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid‐optimization algorithm, which couples a genetic algorithm and successive‐linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater‐management problem. The results indicate that the hybrid‐optimization algorithm can identify the global optimum. The hybrid‐optimization algorithm is then applied to solve a complex groundwater‐management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design

USGS Publications Warehouse

Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter

2012-01-01

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctiveuse strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.

Drug Addiction: Updating Actions to Habits to Compulsions Ten Years On.

PubMed

Everitt, Barry J; Robbins, Trevor W

2016-01-01

A decade ago, we hypothesized that drug addiction can be viewed as a transition from voluntary, recreational drug use to compulsive drug-seeking habits, neurally underpinned by a transition from prefrontal cortical to striatal control over drug seeking and taking as well as a progression from the ventral to the dorsal striatum. Here, in the light of burgeoning, supportive evidence, we reconsider and elaborate this hypothesis, in particular the refinements in our understanding of ventral and dorsal striatal mechanisms underlying goal-directed and habitual drug seeking, the influence of drug-associated Pavlovian-conditioned stimuli on drug seeking and relapse, and evidence for impairments in top-down prefrontal cortical inhibitory control over this behavior. We further review animal and human studies that have begun to define etiological factors and individual differences in the propensity to become addicted to drugs, leading to the description of addiction endophenotypes, especially for cocaine addiction. We consider the prospect of novel treatments for addiction that promote abstinence from and relapse to drug use.

Optimism and spontaneous self-affirmation are associated with lower likelihood of cognitive impairment and greater positive affect among cancer survivors

PubMed Central

Taber, Jennifer M.; Klein, William M. P.; Ferrer, Rebecca A.; Kent, Erin E.; Harris, Peter R.

2016-01-01

Background Optimism and self-affirmation promote adaptive coping, goal achievement, and better health. Purpose To examine the associations of optimism and spontaneous self-affirmation (SSA) with physical, mental, and cognitive health and information seeking among cancer survivors. Methods Cancer survivors (n=326) completed the Health Information National Trends Survey 2013, a national survey of U.S. adults. Participants reported optimism, SSA, cognitive and physical impairment, affect, health status, and information seeking. Results Participants higher in optimism reported better health on nearly all indices examined, even when controlling for SSA. Participants higher in SSA reported lower likelihood of cognitive impairment, greater happiness and hopefulness, and greater likelihood of cancer information seeking. SSA remained significantly associated with greater hopefulness and cancer information seeking when controlling for optimism. Conclusions Optimism and SSA may be associated with beneficial health-related outcomes among cancer survivors. Given the demonstrated malleability of self-affirmation, these findings represent important avenues for future research. PMID:26497697

Optimism and Spontaneous Self-affirmation are Associated with Lower Likelihood of Cognitive Impairment and Greater Positive Affect among Cancer Survivors.

PubMed

Taber, Jennifer M; Klein, William M P; Ferrer, Rebecca A; Kent, Erin E; Harris, Peter R

2016-04-01

Optimism and self-affirmation promote adaptive coping, goal achievement, and better health. The aim of this study is to examine the associations of optimism and spontaneous self-affirmation (SSA) with physical, mental, and cognitive health and information seeking among cancer survivors. Cancer survivors (n = 326) completed the Health Information National Trends Survey 2013, a national survey of US adults. Participants reported optimism, SSA, cognitive and physical impairment, affect, health status, and information seeking. Participants higher in optimism reported better health on nearly all indices examined, even when controlling for SSA. Participants higher in SSA reported lower likelihood of cognitive impairment, greater happiness and hopefulness, and greater likelihood of cancer information seeking. SSA remained significantly associated with greater hopefulness and cancer information seeking when controlling for optimism. Optimism and SSA may be associated with beneficial health-related outcomes among cancer survivors. Given the demonstrated malleability of self-affirmation, these findings represent important avenues for future research.

Peak reduction for commercial buildings using energy storage

NASA Astrophysics Data System (ADS)

Chua, K. H.; Lim, Y. S.; Morris, S.

2017-11-01

Battery-based energy storage has emerged as a cost-effective solution for peak reduction due to the decrement of battery’s price. In this study, a battery-based energy storage system is developed and implemented to achieve an optimal peak reduction for commercial customers with the limited energy capacity of the energy storage. The energy storage system is formed by three bi-directional power converter rated at 5 kVA and a battery bank with capacity of 64 kWh. Three control algorithms, namely fixed-threshold, adaptive-threshold, and fuzzy-based control algorithms have been developed and implemented into the energy storage system in a campus building. The control algorithms are evaluated and compared under different load conditions. The overall experimental results show that the fuzzy-based controller is the most effective algorithm among the three controllers in peak reduction. The fuzzy-based control algorithm is capable of incorporating a priori qualitative knowledge and expertise about the load characteristic of the buildings as well as the useable energy without over-discharging the batteries.

Real-Time Adaptive Control of Flow-Induced Cavity Tones

NASA Technical Reports Server (NTRS)

Kegerise, Michael A.; Cabell, Randolph H.; Cattafesta, Louis N.

2004-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. The adaptive control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. The algorithm was also able t o maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Controller performance was evaluated with a measure of output disturbance rejection and an input sensitivity transfer function. The results suggest that disturbances entering the cavity flow are colocated with the control input at the cavity leading edge. In that case, only tonal components of the cavity wall-pressure fluctuations can be suppressed and arbitrary broadband pressure reduction is not possible. In the control-algorithm development, the cavity dynamics are treated as linear and time invariant (LTI) for a fixed Mach number. The experimental results lend support this treatment.

Robust control algorithms for Mars aerobraking

NASA Technical Reports Server (NTRS)

Shipley, Buford W., Jr.; Ward, Donald T.

1992-01-01

Four atmospheric guidance concepts have been adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. The first two offer improvements to the Analytic Predictor Corrector (APC) to increase its robustness to density variations. The second two are variations of a new Liapunov tracking exit phase algorithm, developed to guide the vehicle along a reference trajectory. These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. MARSGRAM is used to develop realistic atmospheres for the study. When square wave density pulses perturb the atmosphere all four controllers are successful. The algorithms are tested against atmospheres where the inbound and outbound density functions are different. Square wave density pulses are again used, but only for the outbound leg of the trajectory. Additionally, sine waves are used to perturb the density function. The new algorithms are found to be more robust than any previously tested and a Liapunov controller is selected as the most robust control algorithm overall examined.

New mode switching algorithm for the JPL 70-meter antenna servo controller

NASA Technical Reports Server (NTRS)

Nickerson, J. A.

1988-01-01

The design of control mode switching algorithms and logic for JPL's 70 m antenna servo controller are described. The old control mode switching logic was reviewed and perturbation problems were identified. Design approaches for mode switching are presented and the final design is described. Simulations used to compare old and new mode switching algorithms and logic show that the new mode switching techniques will significantly reduce perturbation problems.

An observer-based compensator for distributed delays in integrated control systems

NASA Technical Reports Server (NTRS)

Luck, Rogelio; Ray, Asok

1989-01-01

This paper presents an algorithm for compensation of delays that are distributed within a control loop. The observer-based algorithm is especially suitable for compensating network-induced delays that are likely to occur in integrated control systems of the future generation aircraft. The robustness of the algorithm relative to uncertainties in the plant model have been examined.