A Complex Window-Based Joint-Chirp-Rate-Time-Frequency Transform for BBH Merger Gravitational Wave Signal Detection

With the development of machine-learning algorithms, many attempts have been made to use Artificial Neural Networks (ANN) for complicated tasks related to data classification, pattern recognition, and predictive modeling. Among such applications include Binary Black Hole (BBH) and Binary Neutron Star (BNS) merger Gravitational Wave (GW) signal detection and forecasting. Image neural networks that use time-frequency spectrograms as inputs remain one of the most prominent methods due to their relevance to highly efficient and robust ANN architectures. Earlier studies used traditional Fourier transform-based time-frequency decomposition methods for spectrogram generation, which have had difficulties identifying rapid frequency changes in merger signals with heavy background noise. The primary objective of this study is to develop a signal decomposition technique for improved GW signal classification and detection performance using ANN. We introduce the Joint-Chirp-rate-Time-Frequency transform (JCTFT), in which complex-valued window functions are used to modulate the amplitude, frequency, and phase of the input signal. In addition, we outline general techniques for generating chirp rate enhanced time-frequency spectrograms from the results of a JCTFT. We found improved signal localization performance of the JCTFT in comparison to the short-time-Fourier-transform method with a moderate-to-high amount of background noise. The JCTFT can be applied to existing and next-generation GW detector signals. The inclusion of the chirp rate makes the JCTFT computation more time-consuming. Further studies will aim to improve the efficiency and performance of JCTFT numerical computations.Comment: 14 pages, 6 figure

An innovative vehicle-mounted GPR technique for fast and efficient monitoring of tunnel lining structural conditions

AbstractThe health status of a railway tunnel should be regularly inspected during its service period to ensure safe operation. Ground-penetrating radar (GPR) has been used as a key technique for tunnel detection; however, so far, the measurements of GPR are only obtainable in contact mode. Such methods cannot meet the requirements of the operational tunnel disease census and regular inspections. Therefore, a new method—vehicle-mounted GPR with long-range detection—has been developed. It consists of six channels. The distance from its air-launched antenna to the tunnel lining is approximately 0.93 m–2.25 m. The scanning rate of each channel is 976 1/s. When the sampling point interval is 5 cm, the maximum speed can reach up to 175 km/h. With its speed and air-launched antenna, this system has a significant advantage over existing methods. That is, for an electrified railway, there is no need for power outages. Indeed, the proposed system will not interrupt normal railway operation. Running tests were carried out on the Baoji–Zhongwei and Xiangfan–Chongqing railway lines, and very good results were obtained

Accuracy Analysis of Attitude Computation Based on Optimal Coning Algorithm

To accurately evaluate the applicability of optimal coning algorithms, the direct influence of their periodic components on attitude accuracy is investigated. The true value of the change of the rotation vector is derived from the classical coning motion for analytic comparison. The analytic results show that the influence of periodic components is mostly dominant in two types of optimal coning algorithms. Considering that the errors of periodic components cannot be simply neglected, these algorithms are categorized with simplified forms. A variety of simulations are done under the classical coning motion. The numerical results are in good agreement with the analytic deductions. Considering their attitude accuracy, optimal coning algorithms of the 4-subinterval and 5-subinterval algorithms optimized with angular increments are not recommended for use for real application.Defence Science Journal, 2012, 62(6), pp.361-368, DOI:http://dx.doi.org/10.14429/dsj.62.143

The Role of rr-Modes in Pulsar Spindown, Pulsar Timing and Gravitational Waves

Pulsars are fast spinning neutron stars that lose their rotational energy via various processes such as gravitational and magnetic radiation, particle acceleration and mass loss processes. This dissipation can be quantified by a spin-down equation that measures the rate of change of the frequency as a function of the rotational frequency itself. We explore the pulsar spin-down and consider the spin-down equation upto the seventh order in frequency. This seventh order term accounts for energy loss due to the gravitational radiation caused by a current type quadrupole in the pulsar due to $r$-modes. We derive the rotational frequency due to the $r$-modes and find a solution in terms of the Lambert function. We also present an analytic exact solution for the period from the spindown equation and numerically verify this for the Crab pulsar. This analysis will be relevant for the detection of continuous gravitational waves by 3G ground based and space based gravitational wave detectors

The Role of r-Modes in Pulsar Spindown, Pulsar Timing and Gravitational Waves

Pulsars are fast spinning neutron stars that lose their rotational energy via various processes such as gravitational and magnetic radiation, particle acceleration and mass loss processes. This dissipation can be quantified by a spin-down equation that measures the rate of change of the frequency as a function of the rotational frequency itself. We explore the pulsar spin-down and consider the spin-down equation upto the seventh order in frequency. This seventh order term accounts for energy loss due to the gravitational radiation caused by a current type quadrupole in the pulsar due to r-modes. We derive the rotational frequency due to the r-modes and find a solution in terms of the Lambert function. We also present an analytic exact solution for the period from the spindown equation and numerically verify this for the Crab pulsar. This analysis will be relevant for the detection of continuous gravitational waves by 3G ground based and space based gravitational wave detectors

Filling Conversation Ellipsis for Better Social Dialog Understanding

The phenomenon of ellipsis is prevalent in social conversations. Ellipsis increases the difficulty of a series of downstream language understanding tasks, such as dialog act prediction and semantic role labeling. We propose to resolve ellipsis through automatic sentence completion to improve language understanding. However, automatic ellipsis completion can result in output which does not accurately reflect user intent. To address this issue, we propose a method which considers both the original utterance that has ellipsis and the automatically completed utterance in dialog act and semantic role labeling tasks. Specifically, we first complete user utterances to resolve ellipsis using an end-to-end pointer network model. We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances. Finally, we combine the prediction results from these two utterances using a selection model that is guided by expert knowledge. Our approach improves dialog act prediction and semantic role labeling by 1.3% and 2.5% in F1 score respectively in social conversations. We also present an open-domain human-machine conversation dataset with manually completed user utterances and annotated semantic role labeling after manual completion.Comment: Accepted to AAAI 202

Application of Adaptive Extended Kalman Smoothing on INS/WSN Integration System for Mobile Robot Indoors

The inertial navigation systems (INS)/wireless sensor network (WSN) integration system for mobile robot is proposed for navigation information indoors accurately and continuously. The Kalman filter (KF) is widely used for real-time applications with the aim of gaining optimal data fusion. In order to improve the accuracy of the navigation information, this work proposed an adaptive extended Kalman smoothing (AEKS) which utilizes inertial measuring units (IMUs) and ultrasonic positioning system. In this mode, the adaptive extended Kalman filter (AEKF) is used to improve the accuracy of forward Kalman filtering (FKF) and backward Kalman filtering (BKF), and then the AEKS and the average filter are used between two output timings for the online smoothing. Several real indoor tests are done to assess the performance of the proposed method. The results show that the proposed method can reduce the error compared with the INS-only, least squares (LS) solution, and AEKF