The Reliability and Effectiveness of a Radar-Based Animal Detection System

This document contains data on the reliability and effectiveness of an animal detection system along U.S. Hwy 95 near Bonners Ferry, Idaho. The system uses a Doppler radar to detect large mammals (e.g., deer and elk) when they approach the highway. The system met most of the suggested minimum norms for reliability. The total time the warning signs were activated was at most 90 seconds per hour, and likely substantially less. Animal detection systems are designed to detect an approaching animal. After an animal has been detected, warning signs are activated which allow drivers to respond. Results showed that 58.1–67.9% of deer were detected sufficiently early for northbound drivers, and 70.4–85% of deer were detected sufficiently early for southbound drivers. The effect of the activated warning signs on vehicle speed was greatest when road conditions were challenging (e.g., freezing temperatures and snow- and ice-covered road surface) and when visibility was low (night). In summer, there was no measurable benefit of activated warning signs, at least not as far as vehicle speed is concerned. Depending on the conditions in autumn and winter, the activated warning signs resulted in a speed reduction of 0.69 to 4.43 miles per hour. The report includes practical recommendations for operation and maintenance of the system and suggestions for potential future research

To perform a gyro test of general relativity in a satellite and develop associated control technology

Performance tests of gyroscope operations and gyroscope readout equipment are discussed. The gyroscope was tested for 400 hours at liquid helium temperatures with spin speeds up to 30 Hz. Readout by observing trapped magnetic flux in the spinning rotor with a sensitive magnetometer was accomplished. Application of the gyroscope to space probes and shuttle vehicles

Clipping in Neurocontrol by Adaptive Dynamic Programming

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms

An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time

We consider the adaptive dynamic programming technique called Dual Heuristic Programming (DHP), which is designed to learn a critic function, when using learned model functions of the environment. DHP is designed for optimizing control problems in large and continuous state spaces. We extend DHP into a new algorithm that we call Value-Gradient Learning, VGL(λ), and prove equivalence of an instance of the new algorithm to Backpropagation Through Time for Control with a greedy policy. Not only does this equivalence provide a link between these two different approaches, but it also enables our variant of DHP to have guaranteed convergence, under certain smoothness conditions and a greedy policy, when using a general smooth nonlinear function approximator for the critic. We consider several experimental scenarios including some that prove divergence of DHP under a greedy policy, which contrasts against our proven-convergent algorithm

Diversity maintenance using a population of repelling random-mutation hill climbers

A novel evolutionary algorithm, which can be viewed as an extension to the simple, yet effective, approach of the Random-Mutation Hill Climber (RMHC), is presented. The algorithm addresses the shortcomings of RMHC and its multi-individual parallel version through the introduction of a penalty term into the fitness function, which penalizes individuals in the population for being too similar, hence maintaining population diversity. The performance of the algorithm is evaluated on the deceptive trap and a set of SAT problems, comparing them to the Crowding EA. The results show that at a small cost of solution speed on simpler problems, the algorithm gains better capabilities of dealing with the issues of local maxima

Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network

- Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations. This paper investigates how to mitigate such problems using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through time. The performance of the DP-based neural controller is studied for typical vector control conditions and compared with conventional vector control methods. The paper also investigates how varying grid and power converter system parameters may affect the performance and stability of the neural control system. Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed

Development of a German version of the Oswestry Disability Index. Part 1: cross-cultural adaptation, reliability, and validity

Patient-orientated assessment methods are of paramount importance in the evaluation of treatment outcome. The Oswestry Disability Index (ODI) is one of the condition-specific questionnaires recommended for use with back pain patients. To date, no German version has been published in the peer-reviewed literature. A cross-cultural adaptation of the ODI for the German language was carried out, according to established guidelines. One hundred patients with chronic low-back pain (35 conservative, 65 surgical) completed a questionnaire booklet containing the newly translated ODI, along with a 0-10 pain visual analogue scale (VAS), the Roland Morris Disability Questionnaire, and Likert scales for disability, medication intake and pain frequency [to assess ODI's construct (convergent) validity]. Thirty-nine of these patients completed a second questionnaire within 2weeks (to assess test-retest reliability). The intraclass correlation coefficient for the test-retest reliability of the questionnaire was 0.96. In test-retest, 74% of the individual questions were answered identically, and 21% just one grade higher or lower. The standard error of measurement (SEM) was 3.4, giving a "minimum detectable change” (MDC95%) for the ODI of approximately 9 points, i.e. the minimum change in an individual's score required to be considered "real change” (with 95% confidence) over and above measurement error. The ODI scores correlated with VAS pain intensity (r=0.78, P<0.001) and Roland Morris scores (r=0.80, P<0.001). The mean baseline ODI scores differed significantly between the surgical and conservative patients (P<0.001), and between the different categories of the Likert scales for disability, medication use and pain frequency (in each case P<0.001). Our German version of the Oswestry questionnaire is reliable and valid, and shows psychometric characteristics as good as, if not better than, the original English version. It should represent a valuable tool for use in future patient-orientated outcome studies in German-speaking land

Development of a German version of the Oswestry Disability Index. Part 2: sensitivity to change after spinal surgery

When functional scales are to be used as treatment outcome measures, it is essential to know how responsive they are to clinical change. This information is essential not only for clinical decision-making, but also for the determination of sample size in clinical trials. The present study examined the responsiveness of a German version of the Oswestry Disability Index version 2.1 (ODI) after surgical treatment for low back pain. Before spine surgery 63 patients completed a questionnaire booklet containing the ODI, along with a 0-10 pain visual analogue scale (VAS), the Roland Morris disability questionnaire, and Likert scales for disability, medication intake and pain frequency. Six months after surgery, 57 (90%) patients completed the same questionnaire booklet and also answered Likert-scale questions on the global result of surgery, and on improvements in pain and disability. Both the effect size for the ODI change score 6months after surgery (0.87) and the area under the receiver operating characteristics (ROC) curve for the relative improvement in ODI score in relation to global outcome 6months after surgery (0.90) indicated that the ODI showed good responsiveness. The ROC method revealed that a minimum reduction of the baseline (pre-surgery) ODI score by 18% (equal to a mean 8-point reduction in this patient group) represented the cut-off for indicating a "good” individual outcome 6months after surgery (sensitivity 91.4% and specificity 82.4%). The German version of the ODI is a sensitive instrument for detecting clinical change after spinal surgery. Individual improvements after surgery of at least an 18% reduction on baseline values are associated with a good outcome. This figure can be used as a reliable guide for the determination of sample size in future clinical trials of spinal surger

Neural-network based vector control of VSCHVDC transmission systems

The application of high-voltage dc (HVDC) using voltage-source converters (VSC) has surged recently in electric power transmission and distribution systems. An optimal vector control of a VSC-HVDC system which uses an artificial neural network to implement an approximate dynamic programming algorithm and is trained with Levenberg-Marquardt is introduced in this paper. The proposed neural network vector control algorithm is analyzed in comparison with standard vector control methods for various HVDC control requirements, including dc voltage, active and reactive power control, and ac system voltage support. Assessment of the resulting closed-loop control shows that the neural network vector control approach has superior performance and works efficiently within and beyond the constraints of the HVDC system, for instance, converter rated power and saturation of PWM modulation

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

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