Reliable dual-redundant sensor failure detection and identification for the NASA F-8 DFBW aircraft

A technique was developed which provides reliable failure detection and identification (FDI) for a dual redundant subset of the flight control sensors onboard the NASA F-8 digital fly by wire (DFBW) aircraft. The technique was successfully applied to simulated sensor failures on the real time F-8 digital simulator and to sensor failures injected on telemetry data from a test flight of the F-8 DFBW aircraft. For failure identification the technique utilized the analytic redundancy which exists as functional and kinematic relationships among the various quantities being measured by the different control sensor types. The technique can be used not only in a dual redundant sensor system, but also in a more highly redundant system after FDI by conventional voting techniques reduced to two the number of unfailed sensors of a particular type. In addition the technique can be easily extended to the case in which only one sensor of a particular type is available

Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches

Data Extraction, Transformation, and Loading Process Automation for Algorithmic Trading Machine Learning Modelling and Performance Optimization

A data warehouse efficiently prepares data for effective and fast data analysis and modelling using machine learning algorithms. This paper discusses existing solutions for the Data Extraction, Transformation, and Loading (ETL) process and automation for algorithmic trading algorithms. Integrating the Data Warehouses and, in the future, the Data Lakes with the Machine Learning Algorithms gives enormous opportunities in research when performance and data processing time become critical non-functional requirements

GSU Schedule File Transformation Tools

GSU class schedule can be printed or saved as a text file. This file is not very user friendly to analyze the data and take effective decisions. This text file has section details spanned across multiple pages with headers repeating in each page. This text file also has some additional details in between sections that are not necessarily used. This project “GSU Schedule File Transformation Tools” focuses on creating an online application that provides workable spreadsheet from GSU section schedule text file. This will be done by extracting data from text file, transform the data and load it into excel. Transformed data will also be stored in database for future reference, in case of excel or text files are lost. This project works like online ETL tool for GSU members and provides business intelligent data. Transformed data will help professors, student advisors and students in many ways by simply using the filters in a single glance. Some of the benefits include Current availability across multiple sections To see which sections are filling up fast Waiting list details of different sections Planning sections for any student by days of the week

Complexity of Monadic inf-datalog. Application to temporal logic.

In [11] we defined Inf-Datalog and characterized the fragments of Monadic inf-Datalog that have the same expressive power as Modal Logic (resp. $CTL$, alternation-free Modal $\mu$-calculus and Modal $\mu$-calculus). We study here the time and space complexity of evaluation of Monadic inf-Datalog programs on finite models. We deduce a new unified proof that model checking has 1. linear data and program complexities (both in time and space) for $CTL$ and alternation-free Modal $\mu$-calculus, and 2. linear-space (data and program) complexities, linear-time program complexity and polynomial-time data complexity for $L\mu_k$ (Modal $\mu$-calculus with fixed alternation-depth at most $k$).

Optimization of Lead Base Perovskite Solar Cell with ZnO and CuI as Electron Transport Material and Hole Transport Material Using SCAPS-1D

Perovskite solar cells (PSCs) research is substantially drawing attention because of the fast improvement in their power conversion efficiency (PCE), cheapness, possibility to tune the bandgap, low recombination rate, high open circuit voltage, excellent ambipolar charge carrier transport and strong and broad optical absorption. In this research, Zinc oxide as electron transport material (ETM) and copper iodide as hole transport material (HTM) have been optimized using SCAPS-1D simulation software. The thickness, bandgap, of ZnO (ETM) and CuI (HTM) was investigated. Results shows that the thickness and bandgap were found to strongly influence the PCE of perovskite solar cell. ZnO/CuI   was found to be a better replacement to TiO2/Cu2O for stability and low degradation rate. It was observed that the maximum efficiency is 22.04%, Voc of 0.84V, JSC of 32.83mA/cm2 and FF of 79.79% was obtained when the thickness of ETM and HTM layer of (CH3NH3PbI3) PSCs which was found to be optimum at 0.2μm for the final optimization

Dedicated hippocampal inhibitory networks for locomotion and immobility

Network activity is strongly tied to animal movement; however, hippocampal circuits selectively engaged during locomotion or immobility remain poorly characterized. Here we examined whether distinct locomotor states are encoded differentially in genetically defined classes of hippocampal interneurons. To characterize the relationship between interneuron activity and movement, we usedin vivo, two-photon calcium imaging in CA1 of male and female mice, as animals performed a virtual-reality (VR) track running task. We found that activity in most somatostatin-expressing and parvalbumin-expressing interneurons positively correlated with locomotion. Surprisingly, nearly one in five somatostatin or one in seven parvalbumin interneurons were inhibited during locomotion and activated during periods of immobility. Anatomically, the somata of somatostatin immobility-activated neurons were smaller than those of movement-activated neurons. Furthermore, immobility-activated interneurons were distributed across cell layers, with somatostatin-expressing cells predominantly in stratum oriens and parvalbumin-expressing cells mostly in stratum pyramidale. Importantly, each cell's correlation between activity and movement was stable both over time and across VR environments. Our findings suggest that hippocampal interneuronal microcircuits are preferentially active during either movement or immobility periods. These inhibitory networks may regulate information flow in “labeled lines” within the hippocampus to process information during distinct behavioral states.SIGNIFICANCE STATEMENTThe hippocampus is required for learning and memory. Movement controls network activity in the hippocampus but it's unclear how hippocampal neurons encode movement state. We investigated neural circuits active during locomotion and immobility and found interneurons were selectively active during movement or stopped periods, but not both. Each cell's response to locomotion was consistent across time and environments, suggesting there are separate dedicated circuits for processing information during locomotion and immobility. Understanding how the hippocampus switches between different network configurations may lead to therapeutic approaches to hippocampal-dependent dysfunctions, such as Alzheimer's disease or cognitive decline.</jats:p