Pseudo-random Disturbance Research Articles

Time-Varying Pseudorandom Disturbed Pattern Generation Algorithm for Track Circuit Equipment Testing.

To improve the test accuracy and fault coverage of high-speed railway-related equipment boards, a time-varying pseudorandom disturbance algorithm based on the automatic test pattern generation technology in chip testing is proposed. The algorithm combines the pseudorandom pattern generation algorithm with the deterministic pattern generation D algorithm. The existing pseudorandom number generation method usually requires random seeds to generate a series of pseudorandom numbers. In this algorithm, the system timer is used as the random seed to design a pseudorandom pattern generation method of time-varying seed to improve the randomness of pseudorandom pattern generation. In addition, in combination with the D algorithm, this work proposes a new switching logic between two algorithms by counting invalid pattern proportions. When the algorithm is applied to track a circuit netlist, the fault coverage can reach near 100%. However, the large-scale circuit fault coverage cannot easily reach 100%. The test results for the standard circuits of different sizes show that at the same time, compared with the independent pattern generation methods, the proposed algorithm can improve fault coverage by more than 50% and 30% and significantly improve the pattern generation efficiency. Therefore, it can be used perfectly in the subsequent construction of high-speed railway equipment test platforms.

The Effect of Noisy Galvanic Vestibular Stimulation on Learning of Functional Mobility and Manual Control Nulling Sensorimotor Tasks.

Galvanic vestibular stimulation (GVS) is a non-invasive method of electrically stimulating the vestibular system. We investigated whether the application of GVS can alter the learning of new functional mobility and manual control tasks and whether learning can be retained following GVS application. In a between-subjects experiment design, 36 healthy subjects performed repeated trials, capturing the learning of either (a) a functional mobility task, navigating an obstacle course on a compliant surface with degraded visual cues or (b) a manual control task, using a joystick to null self-roll tilt against a pseudo-random disturbance while seated in the dark. In the “learning” phase of trials, bilateral, bipolar GVS was applied continuously. The GVS waveform also differed between subjects in each task group: (1) white noisy galvanic vestibular stimulation (nGVS) at 0.3 mA (2) high-level random GVS at 0.7 mA (selected from pilot testing as destabilizing, but not painful), or (3) with the absence of stimulation (i.e., sham). Following the “learning” trials, all subjects were blindly transitioned to sham GVS, upon which they immediately completed another series of trials to assess any aftereffects. In the functional mobility task, we found nGVS significantly improved task learning (p = 0.03, mean learning metric 171% more than the sham group). Further, improvements in learning the functional mobility task with nGVS were retained, even once the GVS application was stopped. The benefits in learning with nGVS were not observed in the manual control task. High level GVS tended to inhibit learning in both tasks, but not significantly so. Even once the high-level stimulation was stopped, the impaired performance remained. Improvements in learning with nGVS may be due to increased information throughput resulting from stochastic resonance. The benefit of nGVS for functional mobility, but not manual control nulling, may be due to the multisensory (e.g., visual and proprioceptive), strategic, motor coordination, or spatial awareness aspects of the former task. Learning improvements with nGVS have the potential to benefit individuals who perform functional mobility tasks, such as astronauts, firefighters, high performance athletes, and soldiers.

A Disturbance-Free Built-In Self-Test and Diagnosis Technique for DC-DC Converters

Complex electronic systems include multiple power domains and drastically varying dynamic power consumption patterns, requiring the use of multiple power conversion and regulation units. High-frequency switching converters have been gaining prominence in the DC-DC converter market due to their high efficiency and smaller form factor. Unfortunately, they are also subject to higher process variations, and faster in-field degradation, jeopardizing stable operation of the power supply. This article presents a technique to track changes in the dynamic loop characteristics of DC-DC converters without disturbing the normal mode of operation using a white noise–based excitation and correlation. Using multiple points for injection and analysis, we show that the degraded part can be diagnosed to take remedial action. White noise excitation is generated via a pseudo-random disturbance at reference, load current, and pulse-width modulation (PWM) nodes of the converter with the test signal energy being spread over a wide bandwidth, without significantly affecting the converter noise and ripple floor. The impulse response is extracted by correlating the random input sequence with the disturbed output generated. Test signal analysis is achieved by correlating the pseudo-random input sequence with the output response and thereby accumulating the desired behavior over time and pulling it above the noise floor of the measurement set-up. An off-the-shelf power converter, LM27402, is used as the device-under-test (DUT) for experimental verification. Experimental results show that the proposed technique can estimate converter natural frequency and quality factor ( Q -factor) within ±2.5% and ±0.7% error margin respectively, over changes in load inductance and capacitance. For the diagnosis purpose, a measure of inductor's DC resistance (DCR) value, which is the inductor's series resistance and indicative of the degradation in inductor's Q -factor, is estimated within less than ±1.6% error margin.

Human Factors in Space - FMPT Manual Control Study Observations -

Microgravity effects on human factor were observed through a series of manual control experiments conducted in the First Material Processing Test. The Japanese Payload Specialist, who operated a stick to control a double integral controlled element against pseudo-random disturbance, felt pain in fast eye movement during orbital experiments. He was obliged to fix his line of sight at the center of display and to watch the displayed error movement using peripheral view. He also felt difficulty in supporting his body against reaction force of his hand movement. During two or three days after landing, disturbance was observed in the PS's posture. The operator's input and output data wiIl be analyzed by the cross spectral analysis to obtain the operator describing function and the remnants. Such data wiII be utilized in space man-machine system design. EIectro-myogram of hand and electro-oculogram data will also be analyzed.

On-Line Identification of Power Generation Plant

Mathematical models of electric power plants are needed to predict the response of the plant to events which occur on the power system, and in the development of improved control schemes. Analytical models suffer from difficulty in obtaining para-meters, and in updating them with changing plant conditions. The present work involves deriving models of boilers and turbogenerators directly from plant measurements during tests. These consist of injecting small pseudo-random disturbances to the plant, and recording its responses to these known inputs. A model is then derived by processing the data using identification algorithms. Extensive tests have been conducted on full-scale plant, to establish acceptable data-acquisition and identification procedures, identify suitable model structures, and determine their range of validity. The paper describes the methods adopted. Test and identification results are presented, showing that accurate models of plant may be obtained.

Parametric analysis of dynamic postural responses.

A detailed theoretical understanding of postural control mechanisms must be preceded by careful quantification of both the deterministic and stochastic aspects of postural behavior of normal and abnormal subjects under various dynamic conditions. Toward this end, concise parametric transfer function plus noise models were derived for both shoulder and waist position data obtained by applying a linear anterior-posterior bandlimited pseudorandom disturbance to the base of support of human subjects. Model orders as well as model parameters were determined empirically. One advantage of this modeling procedure is the conciseness of the postural models, permitting easy statistical analysis of the data obtained under different dynamic conditions from many subjects. Model features, including pole and zero locations, from 6 normal subjects each tested on 5 consecutive days under 3 input amplitudes and eyes open and closed conditions are presented. The resulting transfer function models consist of only 1 or 2 poles near the integration position on the Z plane unit circle and 0 to 2 zeros. Locations of the poles indicate that the eyes closed responses are more oscillatory, less damped, and with higher gains than the eyes open responses. These transfer functions are similar to nonparametric ones of other authors. The noise model orders are also small. Their spectra are those of low pass systems. Also, the quantity and frequency range of the postural noise is positively related to the amplitude of platform motion as well as related to the presence or absence of vision.