Inspection System And Method For Bond Detection And Validation Of Surface Mount Devices Using Sensor Fusion And Active Perception

A hybrid surface mount component inspection system which includes both vision and infrared inspection techniques to determine the presence of surface mount components on a printed wiring board, and the quality of solder joints of surface mount components on printed wiring boards by using data level sensor fusion to combine data from two infrared sensors to obtain emissivity independent thermal signatures of solder joints, and using feature level sensor fusion with active perception to assemble and process inspection information from any number of sensors to determine characteristic feature sets of different defect classes to classify solder defects.Georgia Tech Research Corporatio

Intelligent voltage dip mitigation in power networks with distributed generation

Includes bibliographical references.The need for ensuring good power quality (PQ) cannot be over-emphasized in electrical power system operation and management. PQ problem is associated with any electrical distribution and utilization system that experiences any voltage, current or frequency deviation from normal operation. In the current power and energy scenario, voltage-related PQ disturbances like voltage dips are a fact which cannot be eliminated from electrical power systems since electrical faults, and disturbances are stochastic in nature. Voltage dip tends to lead to malfunction or shut down of costly and mandatory equipment and appliances in consumers’ systems causing significant financial losses for domestic, commercial and industrial consumers. It accounts for the disruption of both the performance and operation of sensitive electrical and electronic equipment, which reduces the efficiency and the productivity of power utilities and consumers across the globe. Voltage dips are usually experienced as a result of short duration reduction in the r.m.s. (r.m.s.- root mean square) value of the declared or nominal voltage at the power frequency and is usually followed by recovery of the voltage dip after few seconds. The IEEE recommended practice for monitoring electric power quality (IEEE Std. 1159-2009, revised version of June 2009), provides definitions to label an r.m.s. voltage disturbance based upon its duration and voltage magnitude. These disturbances can be classified into transient events such as voltage dips, swells and spikes. Other long duration r.m.s. voltage variations are mains failures, interruption, harmonic voltage distortion and steady-state overvoltages and undervoltages. This PhD research work deals with voltage dip phenomena only. Initially, the present power network was not designed to accommodate renewable distributed generation (RDG) units. The advent and deployment of RDG over recent years and high penetration of RDG has made the power network more complex and vulnerable to PQ disturbances. It is a well-known fact that the degree of newly introduced RDG has increased rapidly and growing further because of several reasons, which include the need to reduce environmental pollution and global warming caused by emission of carbon particles and greenhouse gases, alleviating transmission congestion and loss reduction. RDG ancillary services support especially voltage and reactive power support in electricity networks are currently being recognized, researched and found to be quite useful in voltage dip mitigation

PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers

We introduce an efficient and straightforward technique for rapidly detecting gravitational waves from compact binary mergers. We show that this method achieves the low latencies required to alert electromagnetic partners of candidate binary mergers, aids in data monitoring, and makes use of multidetector networks for sky localization. This approach was instrumental to the analysis of gravitational-wave candidates during the second observing run of Advanced LIGO, including the period of coincident operation with Advanced Virgo, and in particular the analysis of the first observed binary neutron star merger GW170817, where it led to the first tightly localized sky map ($31~\mathrm{deg}^2$) used to identify AT 2017gfo. Operation of this analysis also enabled the initial discovery of GW170104 and GW170608 despite non-nominal observing of the instrument.Comment: 10 pages, 5 figures, submitted to Physical Review

Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness

Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and wakefulness in 1 second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD.Comment: 40 pages, 13 figures, 9 table

Instrumentation for Biological Research, Volume I, Sections 1 to 3 Final Report, Nov. 9, 1964 - Mar. 31, 1966

Bioinstrumentation for controlling and measuring parameters interacting with biological syste

Health Management and Adaptive Control of Distributed Spacecraft Systems

As the development of challenging missions like on-orbit construction and collaborative inspection that involve multi-spacecraft systems increases, the requirements needed to improve post-failure safety to maintain the mission performance also increases, especially when operating under uncertain conditions. In particular, space missions that involve Distributed Spacecraft Systems (e.g, inspection, repairing, assembling, or deployment of space assets) are susceptible to failures and threats that are detrimental to the overall mission performance. This research applies a distributed Health Management System that uses a bio-inspired mechanism based on the Artificial Immune System coupled with a Support Vector Machine to obtain an optimized health monitoring system capable of detecting nominal and off-nominal system conditions. A simulation environment is developed for a fleet of spacecraft performing a low-Earth orbit inspection within close proximity of a target space asset, where the spacecraft observers follow stable relative orbits with respect to the target asset, allowing dynamics to be expressed using the Clohessy-Wiltshire-Hill equations. Additionally, based on desired points of inspection, the observers have specific attitude requirements that are achieved using Reaction Wheels as the control moment device. An adaptive control based on Deep Reinforcement Learning using an Actor-Critic-Adverse architecture is implemented to achieve high levels of mission protection, especially under disturbances that might lead to performance degradation. Numerical simulations to evaluate the capabilities of the health management architecture when the spacecraft network is subjected to failures are performed. A comparison of different attitude controllers such as Nonlinear Dynamic Inversion and Pole Placement against Deep Reinforcement Learning based controller is presented. The Dynamic Inversion controller showed better tracking performance but large control effort, while the Deep Reinforcement controller showed satisfactory tracking performance with minimal control effort. Numerical simulations successfully demonstrated the potential of both the bioinspired Health Monitoring System architecture and the controller, to detect and identify failures and overcome bounded disturbances, respectively