The Correlation of Pedal Position to Tail Rotor Power Requirement on the OH-58A+

Every mechanical component has a finite failure life. Excessive usage of a component may cause damage. Therefore, life determination is essential to safe operation. Replacement of mechanical components prior to the end of useful life results in higher costs of maintenance. Monitoring rotating components is not always reliable or cost effective. This thesis attempts to solve this problem for a helicopter tail rotor. The development of an algorithm to calculate the loads on a component using stationary measurements can eliminate the need for rotating measurement equipment. The purpose is to predict the tail rotor power requirement of a helicopter throughout the mission profile by using the algorithm developed. The scope of this investigation was limited to representative flight regimes based upon a survey of Bell 206 jet rangers or OH-58A derivatives operators. Flight test data were collected at University of Tennessee Space Institute (UTSI). These data were then used to correlate with the results of the algorithm model. The model incorporated helicopter blade element theory, momentum theory, fin blockage effects, inertia effects, translational velocity effects, mechanical losses and altitude density correction. The model was executed using Microsoft Excel. The model required input data including helicopter characteristics, altitude, engine shaft horsepower, velocity. Based upon these data, the tail rotor power and pedal position were calculated. The calculation results of pedal position were compared with the flight test data. The accuracy of the tail rotor power was presented in the percentage of the maximum tail rotor power. It was found that the pedal position alone was a poor indicator of tail rotor power. The modeled elements mentioned above were included and resulted in significant improvement. Based on the calculated results, this model and measured pedal positions can provide at least 90-% confidence of the tail rotor power, except at large sideslip angles. Additionally, due to the tail rotor power sensitivity to the pedal position variations, the measurement of pedal positions should be performed precisely. Based on the results of this investigation, utilizing the algorithm model for predicting tail rotor power from pedal position will result in a 90-% confidence of the tail rotor power being applied. Recommend an alternative fuselage yaw moment due to sideslip chart be used to improve sideslip data. Recommend instrumented tail rotor for tail rotor power measurements

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed, and minor updates made in the Appendix (v2

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation

In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the multi-timescale control and communications (MTCC) problem in Cellular Vehicle-to-Everything (C-V2X) system into a communication-aware Deep Reinforcement Learning (DRL)-based platoon control (PC) sub-problem and a control-aware DRL-based radio resource allocation (RRA) sub-problem. We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this paper (Part II), we first focus on the RRA sub-problem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multi-agent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multi-timescale control and communications (MTCC) based on Deep Reinforcement Learning (DRL). In this paper (Part I), we first decompose the problem into a communication-aware DRL-based PC sub-problem and a control-aware DRL-based RRA sub-problem. Then, we focus on the PC sub-problem assuming an RRA policy is given, and propose the MTCC-PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov Decision Process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC-PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of C-V2X communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC-PC with those of the baseline DRL algorithms

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the number of votes or pageviews, which can directly tell us about the importance of entities in a KG. While several methods have been developed to tackle this problem, their use of these external signals has been limited as they are not designed to consider multiple signals simultaneously. In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts. Also, MultiImport provides an effective estimator based on attentive graph neural networks. We ran experiments on real-world KGs to show that MultiImport handles several challenges involved with inferring node importance from multiple input signals, and consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100 than the state-of-the-art method.Comment: KDD 2020 Research Track. 10 page

Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia

The Community Multiscale Air Quality (CMAQ) model has been further developed in terms of simulating natural wind-blown dust in this study, with a series of modifications aimed at improving the model\u27s capability to predict the emission, transport, and chemical reactions of dust. The default parameterization of initial threshold friction velocity constants are revised to correct the double counting of the impact of soil moisture in CMAQ by the reanalysis of field experiment data; source-dependent speciation profiles for dust emission are derived based on local measurements for the Gobi and Taklamakan deserts in East Asia; and dust heterogeneous chemistry is also implemented. The improved dust module in the CMAQ is applied over East Asia for March and April from 2006 to 2010. The model evaluation result shows that the simulation bias of PM10 and aerosol optical depth (AOD) is reduced, respectively, from −55.42 and −31.97 % by the original CMAQ to −16.05 and −22.1 % by the revised CMAQ. Comparison with observations at the nearby Gobi stations of Duolun and Yulin indicates that applying a source-dependent profile helps reduce simulation bias for trace metals. Implementing heterogeneous chemistry also results in better agreement with observations for sulfur dioxide (SO2), sulfate (SO42−), nitric acid (HNO3), nitrous oxides (NOx), and nitrate (NO3−). The investigation of a severe dust storm episode from 19 to 21 March 2010 suggests that the revised CMAQ is capable of capturing the spatial distribution and temporal variation of dust. The model evaluation also indicates potential uncertainty within the excessive soil moisture used by meteorological simulation. The mass contribution of fine-mode particles in dust emission may be underestimated by 50 %. The revised CMAQ model provides a useful tool for future studies to investigate the emission, transport, and impact of wind-blown dust over East Asia and elsewhere

The opportunity recognition framework in the Hong Kong SMEs context

This paper presents a preliminary framework of opportunity recognition in the Hong Kong small and medium enterprises (SMEs) context. Guanxi and four trait variables, namely self-monitoring, extroversion, selfefficacy and creativity are the independent variables while the number of opportunity recognized by entrepreneurs is the dependent variable in the framework. The model indicates a mediation effect of guanxi between self-monitoring and the number of opportunities recognized, and between extroversion and the number of opportunities recognized. Meanwhile, SMEs marketing characteristics are determined by personalities and behaviour of the entrepreneurs as they do not conform to the traditional marketing theories (Gilmore et al., 2001). This paper provides new research directions to the field of SMEs marketing

Stability of CubeSat Clocks and Their Impacts on GNSS Radio Occultation

Global Navigation Satellite Systems’ radio occultation (GNSS-RO) provides the upper troposphere-lower stratosphere (UTLS) vertical atmospheric profiles that are complementing radiosonde and reanalysis data. Such data are employed in the numerical weather prediction (NWP) models used to forecast global weather as well as in climate change studies. Typically, GNSS-RO operates by remotely sensing the bending angles of an occulting GNSS signal measured by larger low Earth orbit (LEO) satellites. However, these satellites are faced with complexities in their design and costs. CubeSats, on the other hand, are emerging small and cheap satellites; the low prices of building them and the advancements in their components make them favorable for the GNSS-RO. In order to be compatible with GNSS-RO requirements, the clocks of the onboard receivers that are estimated through the precise orbit determination (POD) should have short-term stabilities. This is essential to correctly time tag the excess phase observations used in the derivation of the GNSS-RO UTLS atmospheric profiles. In this study, the stabilities of estimated clocks of a set of CubeSats launched for GNSS-RO in the Spire Global constellation are rigorously analysed and evaluated in comparison to the ultra-stable oscillators (USOs) onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites. Methods for improving their clock stabilities are proposed and tested. The results (i) show improvement of the estimated clocks at the level of several microseconds, which increases their short-term stabilities, (ii) indicate that the quality of the frequency oscillator plays a dominant role in CubeSats’ clock instabilities, and (iii) show that CubeSats’ derived UTLS (i.e., tropopause) atmospheric profiles are comparable to those of COSMIC-2 products and in situ radiosonde observations, which provided external validation products. Different comparisons confirm that CubeSats, even those with unstable onboard clocks, provide high-quality RO profiles, comparable to those of COSMIC-2. The proposed remedies in POD and the advancements of the COTS components, such as chip-scale atomic clocks and better onboard processing units, also present a brighter future for real-time applications that require precise orbits and stable clocks

External Application of Traditional Chinese Medicine for Venous Ulcers: A Systematic Review and Meta-Analysis

Objective. To evaluate the effectiveness of external application of traditional Chinese medicine (EA-TCM) on venous ulcers. Methods. Seven databases were searched until April 2015 for randomized controlled trials (RCTs) of EA-TCM for venous ulcers. Risk of bias was assessed using Cochrane Handbook guidelines. Study outcomes were presented as risk ratios (RRs) for dichotomous data or mean differences (MDs) for continuous data. Results. Sixteen of 193 potentially relevant trials met the inclusion criteria; however, their methodological qualities were low. Comparison of the same intervention strategies revealed significant differences in total effectiveness rates between EA-TCM and conventional therapy groups (RR = 1.22, 95% confidence interval [CI] = 1.16–1.29, and P<0.00001). Compared to conventional therapy, EA-TCM combined with conventional therapy had a superior total effectiveness rate (RR = 1.11, 95% CI = 1.04–1.19, and P=0.003). There were no significant differences in recurrence rates during followup and final pain measurements between the experimental and those in the control groups (RR = 0.86, 95% CI = 0.31–2.39, and P=0.85; MD −0.75, 95% CI = −2.15–0.65, and P=0.29). Conclusion. The evidence that EA-TCM is an effective treatment for venous ulcers is encouraging, but not conclusive due to the low methodological quality of the RCTs. Therefore, more high-quality RCTs with larger sample sizes are required