Time Difference Model Research Articles

Improved real-time cycle-slip detection for Low Earth Orbit satellites based on the dynamic force model

Cycle-slip detection is crucial in achieving high-accuracy Global Navigation Satellite System (GNSS) data processing for Low Earth Orbit (LEO) satellites. The detector based on the dynamic force model has emerged as a promising approach to cycle-slip detection, as it is insensitive to the number of visible tracked satellites and insufficient accuracy of a prior orbit. However, the ionospheric delay limits the applications of the method for high-speed LEO satellites under different scenarios. To complete the method, we propose a new combination of two test parameters: the phase ionosphere-free (IF) combinations in the second-order time-difference model and the Melbourne–Wübbena (MW) combinations. Meanwhile, we highlight the identification of whether the fake-positive cycle slips are derived from the flicker noises and jumps in the receiver quartz-based clock. Considering highly, moderately active and quiet ionospheric activities, comparisons are performed among our proposed two test parameters and the phase second-order time-difference wide-lane (WL) combinations. The results indicate that the detector based on the IF combinations performs optimally under the three levels of ionospheric activities. The time-difference ionospheric delays vary dramatically even under moderately active and quiet ionospheric activities, which leads to the inferiority of the detector based on the WL combinations to one based on the MW combinations. The performances of detectors based on the IF and MW combinations are further evaluated through real-time kinematic orbital determinations comparisons with the Jet Propulsion Laboratory (JPL) precise science orbits (PSO). After using the IF detector, the mean 3-Dimensional (3D) root-mean-squares (RMS) of the orbit differences have slightly decreased by 12% and 6% for the Gravity Recovery and Climate Experiment (GRACE) A and B satellites, respectively. In the case of the GRACE follow-on (GRACE-FO) satellites, the IF detector has achieved a more than 40% improvement over the MW detector. Further, the use of both detectors simultaneously results in a slight improvement in orbit accuracy.

A fast TDOA and VDOA estimation method of the wideband frequency hopping signal

In the mobile multi-station passive positioning scenario, the carrier frequency hopping of the wideband frequency hopping communication signal will produce a changing Doppler frequency shift. Therefore, it is not possible to directly estimate the Doppler frequency difference of the observed signal containing multiple pulses. In this case, only the time difference and frequency difference of the single-hop signal can be estimated, but the accuracy is low. The existing methods often need the carrier frequency of each FH pulse signal as a priori information, and the high computational complexity of the two-dimensional search method of the time difference and frequency difference is difficult to overcome. Aiming at the above problems, a fast and high-precision estimation algorithm for the time difference of arrival and velocity difference of wideband frequency hopping signals is proposed in this paper. Firstly, the wideband frequency hopping signal is segmented in the time domain with multiple pulses as the unit, the signal model of time-varying time difference is established, and the time difference of each short-time segment is estimated by the generalized cross-correlation algorithm. The estimated values of the initial time difference and Doppler velocity difference can be worked out by least squares fitting of the time difference sequence obtained from the single pulse estimation. Finally, taking the Link16 data link signal as an example, the simulation experiment of the proposed method is conducted, which verifies the correctness and performance superiority of the algorithm.

Modified Model of Sound Velocity with Different Saturation in Fractured Sandstone

The hazards of surrounding rock sheeting, collapse and rock explosion during the excavation of underground projects can be regarded as the macroscopic dynamics of the evolutionary development of their internal fractures, mostly accompanied by acoustic emission phenomena. The application of acoustic emission detection technology can quickly determine the existence of fissures in the surrounding rock and predict their approximate location and spatial spread. Therefore, considering the effect of fissures on the sound velocity propagation law. In this work, experiments on the identification of acoustic emission signal paths in solid media with different void states are carried out, and the path propagation law of acoustic emission signals is explored and studied. A comparative analysis of acoustic emission source localization in fractured sandstone with different sensor arrays at different saturation levels was carried out using water as the coupling agent. The acoustic emission source 3D localization results are optimized by correcting the time difference model. The results show that the acoustic emission signal propagation conforms to the shortest distance principle. In the localization of 3D cylindrical AE sources, it is suitable to select a combined array of spatial tetrahedral sensors for better localization. As the saturation increases the positioning effect gets closer to the actual value. The sound source localization effect of the sound velocity correction model based on the time difference method is closer to the actual lead break position. In actual engineering, water as a benign coupling agent can better improve the accuracy of AE source localization in fracture-containing sandstone, which can provide some guiding suggestions for related engineering.

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

Key performance index estimation based on ensemble locally weighted partial least squares and its application on industrial nonlinear processes

Recent decades have witnessed a trend that soft sensing, instead of hard sensing, has been extensively applied to estimate the key performance indices under the circumstances that practical measurements are hardly to be achieved at a reasonable cost. However, due to the existence of nonlinearities and time-varying characteristics in the practical industrial processes, the conventional soft sensor models probably suffer from severe performance degradations when the original designed models are mismatched. Although many novel methodologies have been employed to alleviate this problem, each of them merely focuses on certain aspect of model features, a comprehensive framework combining these features is needed. Therefore, this study proposes an online predictive methodology based on an integration of ensemble learning based on a novel adaptive locally weighted partial least squares. Specifically, sub-models established on the respective dataset are generated by moving window model, time difference model and just-in-time learning model for the sake of different properties in processes. The effectiveness of the proposed model is validated on the practical nonlinear processes represented by a benchmark simulation model No.1 (BSM1), in wastewater treatment plants (WWTP), and a real industrial catalytic reforming process.

A Multi-Step Source Localization Method With Narrowing Velocity Interval of Cyber-Physical Systems in Buildings

The localization for sources in the heterogeneous and complex media is of vital significance, which can be applied to monitor the invisible cracks and determine the potential damage areas of buildings with safety hazards. Based on the localization function with the model of arrival time difference (TD), a multi-step localization method (MLM) without premeasured velocity for heterogeneous and complex propagation media was proposed. The velocity interval used for localization was narrowed and optimized continuously through the multi-step localization, where the optimal velocity interval was determined when the velocity differences were less than the threshold. Then, the optimal localization results with higher accuracy corresponding to this velocity interval can be obtained with the TD algorithm. A source locating test was performed at a building of masonry structure. In addition, the locating accuracy was compared and discussed between the optimized MLM, the one-step method without premeasured velocity, and the traditional method with different premeasured velocity values. Results show that MLM is obviously superior to both the one-step method and the traditional method. The developed MLM can not only eliminate the errors caused by premeasured velocity, but also can improve the locating accuracy in the heterogeneous and complex media, which is an efficient and effective method for engineering applications.

Mathematical model of time difference for Coriolis flow sensor output signals under gas-liquid two-phase flow

Abstract The features of Coriolis mass flow sensor output signal are analyzed, and a mathematical model of the time difference for the signals is built to evaluate the rationality and limitation of the proposed correction method when Coriolis mass flowmeter (CMF) is used to measure the gas-liquid two-phase flow. According to the experimental data of Coriolis mass flow sensor output signals under the gas-liquid two-phase flow condition, the time differences of the sensor output signals are extracted as a characteristic quantity to obtain the time difference sequence by adopting a digital zero-crossing detection method. The distribution pattern of the time difference sequence is analyzed with the probability density function, and the mathematical model of the time difference sequence is built by the correlation analysis. The mathematical model consists of a stable component and a fluctuating component. Correcting the stable component can improve the measurement accuracy of cumulative mass flow rate, and reducing the fluctuating component can enhance the measurement stability of instantaneous mass flow rate. Although the stable component cannot be completely corrected because its variation is nonlinear with the flow rate and drop in density, and the fluctuating component can only be reduced to a certain extent due to the real-time processing limitation of the instrument, CMF can provide relatively reliable measurement results of the liquid mass flow rate when the gas-liquid two-phase flow occurs.

Spatio‐temporal adaptive soft sensor for nonlinear time‐varying and variable drifting processes based on moving window LWPLS and time difference model

AbstractIndustrial plants often undergo different kinds of changes like variable drifts and time‐variant problems, which may cause the degradation of soft sensors. In this paper, a spatio‐temporal adaptive soft sensor modeling framework, which is based on the moving window and just‐in‐time learning (JITL) techniques, is proposed for nonlinear time‐varying processes. The JITL (locally weighted partial least squares) can adapt the model by spatial weighting technique, and the moving window technique can adapt the soft sensor model to the new process state. Furthermore, time difference (TD) model is utilized to handle the process change of variable drifts. Case studies are carried out on a numerical example and two industrial processes. The results show the effectiveness and flexibility of the proposed soft sensor framework. © 2015 Curtin University of Technology and John Wiley & Sons, Ltd.

Moving Window and Just-in-Time Soft Sensor Model Based on Time Differences Considering a Small Number of Measurements

Soft sensors can predict values of a process variable y that is difficult to measure in real time. Adaptive mechanisms are applied to soft sensors to maintain their predictive ability. However, traditional adaptive soft sensors need a significant number of new y measurements. It is difficult to maintain the accuracy if the measurement interval is large. We propose two soft sensor models that produce accurate results with a small number of y measurements. We combined either a moving window technique or a just-in-time technique with a time difference model to handle changes of the slope between input variables X and y and shifts in X and y values. We analyzed a numerical simulation data set and a real industrial data set, demonstrating the superiority of the time difference model combined with a moving window technique.

Selective Use of Adaptive Soft Sensors Based on Process State

Soft sensors are widely used to realize efficient operations in chemical processes because some governing variables, such as product quality, cannot be measured directly through hardware in real time. One of the design problems of soft sensors is the degradation of their prediction accuracy. To reduce degradation, a range of adaptive models has been developed, such as moving window, just-in-time, and time difference models. However, none of these adaptive models performs well in all process states. To address this problem, we developed an online monitoring system using multivariate statistical process control to select the appropriate adaptive model for each process state. The proposed method was applied to dynamic simulation data and empirical industrial data. Higher predictive accuracy than from traditional adaptive models was achieved. This novel approach may be used to reduce the maintenance cost of soft sensors.

Application of online support vector regression for soft sensors

Soft sensors have been widely used in chemical plants to estimate process variables that are difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes in state of chemical plants. Characteristics of adaptive soft sensor models such as moving window models, just‐in‐time models and time difference models were previously discussed. The predictive accuracy of any traditional models decreases when sudden changes in processes occur. Therefore, a new soft sensor method based on online support vector regression (SVR) and the time variable was developed for constructing soft sensor models adaptive to rapid changes of relationships among process variables. A nonlinear SVR model with the time variable is updated with the most recent data. The proposed method was applied to simulation data and real industrial data, and achieved higher predictive accuracy than traditional ones even when time‐varying changes in process characteristics happen. © 2013 American Institute of Chemical Engineers AIChE J 60: 600–612, 2014

Correction for “Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors”

Correction for “Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors”

Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors

In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, autocorrelation in process variables, predictive accuracy, and so on. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models and the effects of noise, autocorrelation, TD intervals, and so on on the predictive accuracy. The relationships and the formulas were verified by analyzing simulation data. Furthermore, we analyzed dynamic simulation data and real industrial data and confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.

사막 전갈의 진동 감지 행동을 모델로 한 진원지 방향 추정 기법

Abstract: Sand scorpions are nocturnal animals to mostly use tactile senses to detect their prey. It has been reported that sand scorpions have high vibration sensitivity for their prey-localizing behavior. We tested vibration experiments in the sand with microphone sensors to model the sand scorpion’s behavior and a time-difference model was applied to find the direction of a vibration source. Using the information of the arrival time of the vibration signal to reach each leg position, we can find the location of the vibration source. Keywords: sand scorpions, sensor, epicenter estimation, vibration Copyright© ICROS 2012 I. 서론   . !#$%&'( )*+&,-./ '(012 3* &456/7 , 89:; ?@A :BCDEF89GH:IJBKdefg]:/ BChGH:KijKd @:o Bkd GH:IJBy :/ B&zO { , |uOJQ#9}~€M‚ ƒ* . „ †9 &'‡‚ &ˆ‰Š9'‡k‹ ŒoY89:/ BC< o BC <Ž  IJB&z r . '‡OPQˆ‰9 Q. B&z‘’“ . CW'‡OPQˆ‰”1&40~501• !–QRSOPQ '&—˜™”1&šš701:›•œ%1&0v

A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, their predictive accuracy gradually decreases with changes in the state of the plants. We have been constructing soft sensor models based on the time difference of an objective variable, y , and that of explanatory variables (time difference models) for reducing the effects of deterioration with age such as the drift without model reconstruction. In this paper, we have attempted to improve and estimate the prediction accuracy of time difference models, and proposed to handle multiple y -values predicted from multiple intervals of time difference. A weighted average is a final predicted value and the standard deviation is an index of its prediction accuracy. This method was applied to real industrial data and then, could predict more number of data with higher predictive accuracy and estimate the prediction errors more accurately than traditional ones.

Development of high predictive soft sensor method and the application to industrial polymer processes

ABSTRACTIn industrial plants, soft sensors are widely used to estimate process variables that are difficult to measure online. One of the problems of soft sensors is that their predictive accuracy gradually decreases with changes in the state of the plants. Although regression models are reconstructed with new data to solve this problem, some problems remain in practice. Hence, it is attempted to construct soft sensor models based upon the time difference of an objective variable and that of explanatory variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. However, the time difference model cannot account for the nonlinearity in process variables. Therefore, to consider the nonlinearity and the effects of changes with age, we have proposed to construct time difference models after modeling nonlinear relationship between and among process variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these variables. First, we verified the superiority of the proposed methods over traditional ones. Then, we applied these methods to the actual industrial data obtained during an industrial polymer process. The proposed models achieved high predictive accuracy for melt flow rate and density, and the bias of the prediction errors could be eased in both cases by using the proposed methods. We confirmed the usefulness of the proposed methods without reconstruction of soft sensor models. © 2011 Curtin University of Technology and John Wiley &amp; Sons, Ltd.

Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship

Soft sensors are widely used to estimate process variables that are difficult to measure online. Though regression models are reconstructed with new data to adapt changes of the plants, some problems remain in practice. Hence, it is attempted to construct soft sensor models based on the time difference of an objective variable and that of explanatory variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. In this paper, we have proposed to construct time difference models after modeling nonlinear relationship between and among process variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these variables. We applied these methods to the actual industrial data obtained during an industrial polymer process and confirmed the usefulness of the proposed methods.

A physiologically constrained van Bergeijk model of interaural time difference coding predicts bandwidth‐dependent lateralization.

Recent physiological data have revived the notion that ITD is encoded centrally by the relative activity of coarsely tuned channels that pool the firing rates of many neurons [McAlpine et al., Nat. Neurosci. 4, 396–401 (2001), van Bergeijk, J. Acoust. Soc. Am. 34, 1431–1437 (1962)]. Here we implement a model of the van Bergeijk‐type and demonstrate that it predicts the dependence of perceived laterality on stimulus bandwidth [Stern et al., J. Acoust. Soc. Am. 84, 156–165 (1988)]. Each model channel comprises a large number of neurons, whose characteristic frequencies and best ITDs (BDs) are distributed to mimic physiological data from cat inferior colliculus. Each neuron cross‐correlates the bandpass‐filtered stimuli to the two ears, and the channel output is simply the summed firing rate of its neurons. The model predicts lateralization as a function of bandwidth, if four channels are used, one representing each medial and lateral superior olive. The outputs of the van Bergeijk‐type code are appropriate for controlling phonotaxis. Its lack of labeled lines minimizes the precision required in the BD distribution and may explain how a viable ITD representation is formed despite the existence of several factors that determine the BD of individual neurons.

Accurate Sound Localization in Reverberant Environments Is Mediated by Robust Encoding of Spatial Cues in the Auditory Midbrain

In reverberant environments, acoustic reflections interfere with the direct sound arriving at a listener's ears, distorting the spatial cues for sound localization. Yet, human listeners have little difficulty localizing sounds in most settings. Because reverberant energy builds up over time, the source location is represented relatively faithfully during the early portion of a sound, but this representation becomes increasingly degraded later in the stimulus. We show that the directional sensitivity of single neurons in the auditory midbrain of anesthetized cats follows a similar time course, although onset dominance in temporal response patterns results in more robust directional sensitivity than expected, suggesting a simple mechanism for improving directional sensitivity in reverberation. In parallel behavioral experiments, we demonstrate that human lateralization judgments are consistent with predictions from a population rate model decoding the observed midbrain responses, suggesting a subcortical origin for robust sound localization in reverberant environments.

Normalization in count-comparison model of interaural time difference decoding

Recent neurophysical studies suggest that binaural decoding is based on count comparison for both ITD and ILD. In such mechanisms, the neural signals are stronger in the auditory pathways leading to the ipsilateral hemisphere when a signal is presented earlier, or with higher level, to the contralateral ear. A computational model is described implementing binaural cue decoding based on count-comparison principles for ITD decoding, which is assumed to occur in medial superior olive (MSO). Pooled response of MSO is modeled as running multiplication between inputs, which are derived from ear canal signals with GTFB filtering and phase-locked impulse generator. The contralateral and ipsilateral inputs to MSO are then convolved with different responses. The model output corresponds well to neurophysiological results. In the earlier version of the model, the MSO output was normalized only with the contralateral signal, as suggested by the neuroanatomy. It has been later found out that the output of MSO model depends on ILD, which is in contradiction with psychoacoustics studies. In this study, it is proposed that the ipsilateral input to MSO is self-normalized, which provides ILD-independent ITD decoding.