Computer program performs aerothermodynamic flight test data correlation

Computer program plots flight test data /stored on magnetic tape during the flight/ with comparative data from other tapes /design and post-flight predictions/. Information as to which measurements are on each tape, the order in which they appear, and the exact time span is supplied by the source of the data

Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality

In this paper, the problem of data correlation-aware resource management is studied for a network of wireless virtual reality (VR) users communicating over cloud-based small cell networks (SCNs). In the studied model, small base stations (SBSs) with limited computational resources act as VR control centers that collect the tracking information from VR users over the cellular uplink and send them to the VR users over the downlink. In such a setting, VR users may send or request correlated or similar data (panoramic images and tracking data). This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. This VR resource allocation problem is formulated as a noncooperative game that allows jointly optimizing the computational and spectrum resources, while being cognizant of the data correlation. To solve this game, a transfer learning algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. Unlike conventional reinforcement learning algorithms that must be executed each time the environment changes, the proposed algorithm can intelligently transfer information on the learned utility, across time, to rapidly adapt to environmental dynamics due to factors such as changes in the users' content or data correlation. Simulation results show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms of delay compared to the Q-learning with data correlation and Q-learning without data correlation. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays.Comment: This paper has been accepted by Asiloma

Wind tunnel/flight data correlation for the Boeing 737-100 transport airplane

A brief wind-tunnel/flight data correlation for the Boeing 737-100 airplane was made. The results showed excellent agreement between wind-tunnel and flight trimmed drag polars at Mach numbers less than 0.67. The wind-tunnel data predicted larger drag increments due to compressibility and a lift-curve slope about 9 percent higher than flight

Faulty sensor detection using data correlation of multivariant sensor reading in smart agriculture with IOT

The Internet of Things (IoT), the idea of getting real-world objects connected with each other, will change the ways we organize, obtain and consume information radically. Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. On the other hand, Sensor fault is critical in smart grids, where controllers rely on healthy measurements from different sensors to determine all kinds of operations. However, when sensor fault happens, missing data and/or bad data can flow into control and management systems, which may lead to potential malfunction or even system failures. This brings the need for Sensor Fault Detection and eliminate this potential fault. This thesis proposes to design a Faulty Sensor Detection Mechanism using the data correlation method of multivariate sensors. This method will be applied to the smart agriculture which uses multi-variate sensors such as moisture sensor, temperature sensor and water sensor in IoT. The data are collected and received by a microcontroller which also can be linked to the internet. According to the algorithm, which applied on the smart agriculture, in case, the system gives No FAULT when the correlation value between (temperature, moisture) and (temperature, water) are negative and positive for (Water, moisture). In other cases. The system has a fault in a sensor when the correlation values between sensors are changed. Also, when the sensor gives a constant reading for a long time the system has got a fault in this sensor. The system got No FAULT when was different in sensors reading and the correlation value between (temperature, moisture) is (-0.33), between (temperature, water) is (-0.16) and (moisture, water) is (0.36). In addition, this system will be connected to the internet through the ESP8266 module. In order to surveillance the system at anytime and anywhere, this system is connected with the cloud (Things board) by using an ESP8266 WiFi network connection. This would allow the system to be more efficient and more reliable in detecting and monitoring the system’s parameters such as the state of sensors. The accuracy of the algorithm for data correlation may be changing depending on the application that wants to detect the faulty sensor in the system and according to how many data that income to the microcontroller per minute and how many data should take to calculate the correlation coefficient. Therefore, for the smart agriculture which it's used in this project, the period is adjusted to give a good diagnose for the sensor as soon as possible

Estimation of tunnel blockage from wall pressure signatures: A review and data correlation

A method is described for estimating low speed wind tunnel blockage, including model volume, bubble separation and viscous wake effects. A tunnel-centerline, source/sink distribution is derived from measured wall pressure signatures using fast algorithms to solve the inverse problem in three dimensions. Blockage may then be computed throughout the test volume. Correlations using scaled models or tests in two tunnels were made in all cases. In many cases model reference area exceeded 10% of the tunnel cross-sectional area. Good correlations were obtained regarding model surface pressures, lift drag and pitching moment. It is shown that blockage-induced velocity variations across the test section are relatively unimportant but axial gradients should be considered when model size is determined

Practical Data Correlation of Flashpoints of Binary Mixtures by a Reciprocal Function: The Concept and Numerical Examples

Simple data correlation of flashpoint data of binary mixture has been developed on a basic of rational reciprocal function. The new approximation requires has only two coefficients and needs the flashpoint temperature of the pure flammable component to be known. The approximation has been tested by literature data concerning aqueous-alcohol solution and compared to calculations performed by several thermodynamic models predicting flashpoint temperatures. The suggested approximation provides accuracy comparable and to some extent better than that of the thermodynamic methods.Comment: 6 pages and 5 tables IN PRESS; Thermal Science vol. 15, issue 3, 201

Correlation of gravimetric and satellite geodetic data Interim progress report, 11 Sep. 1967 - 29 Feb. 1968

Gravimetric and geodetic data correlation for satellite position prediction accuracy with error analysi

Dominant Multipoles in WMAP5 Mosaic Data Correlation Maps

The method of correlation mapping on the full sphere is used to study the properties of the ILC map, as well as the dust and synchrotron background components. An anomalous correlation of the components with the ILC map in the main plane and in the poles of the ecliptic and equatorial coordinate systems was discovered. Apart from the bias, a dominant quadrupole contribution in the power spectrum of the mosaic correlation maps was found in the pixel correlation histogram. Various causes of the anomalous signal are discussed.Comment: 10 pages,11 figure

Robust program for LLSE data correlation of ternary systems

The existing commercial software packages (like Dechema Data Preparation Package (DPP) and the regression utilities of Chemical Engineering simulators like Chemcad) have been widely used and their extensive capabilities are well-known. Nevertheless, and as long as LLE calculations is concerned, they have been designed for the simplest equilibrium behaviour. For example, for ternary systems it is only possible the correlation of type 1 and type 2 LLE data. None of these applications allows for the correlation of type 0 LLE, type 3 LLLE or type 4 LLSE systems. To enable a possible extension of such software, a robust calculation algorithm to compute phase equilibria among condensed phases for all these more complicated behaviours has been developed.Vicepresidency of Research (University of Alicante) and Generalitat Valenciana (GV/2007/125)

Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.Comment: Accepted in AAAI'201