Long-term Temporal Correlation Research Articles

Study on the spatial and temporal correlation and allometric growth mechanism between population aging and carbon emissions in China.

Population aging and carbon emissions are critical issues for China's development. As an enormous complex system, the population and the carbon emission development process have non-negligible differences in time, space, and speed. Therefore, this paper first demonstrates the spatial and temporal correlation between population aging and carbon emissions from 1995 to 2020, then uses the allometric growth analysis model to make a cross-sectional temporal comparison and a vertical spatial comparison of the relationship and development rate of the two, and finally uses the ridge regression model to determine the forces and interaction mechanisms of the factors influencing the relationship between population aging and carbon emissions at allometric rates. The results show that (1) China has a long-term positive temporal correlation effect relationship between population aging and carbon emissions from 1995 to 2020, and the overall correlation is high. The spatial correlation intensity between population aging and carbon emissions varies significantly across Chinese provinces, with a general spatial distribution trend of high in the south, low in the north, and prominent in the center. (2) China's population aging and carbon emissions mainly show a negative allometric growth type of relationship, i.e., a strong trend of population aging expansion and a strengthening trend of carbon emission system shrinking. The number of provinces with negative allometric growth is gradually increasing, mainly in North, East, Central, and Southwest China. (3) From 1995-2010 period to the 2011-2020 period, the influence of the factors of the population, production, and economic dimensions on the population aging index and the carbon emission allometric scalar index gradually weakened, and the influence of the consumption and technology dimensions increased significantly. The factors on the population and consumption side of the dimension mainly contribute to the expansion of carbon emissions and drive positive allometric growth. The production side, the economic structure, and technology dimension factors drive negative allometric growth. The paper fully explores the bidirectional correlation, differential development trend, and interaction mechanism between the two systems of population and carbon emissions and effectively compensates for the lack of research content in terms of elemental correlation, spatial and temporal connection, and speed synergy.

Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling

Aiming at the problem that existing wind turbine gearbox fault prediction models often find it difficult to distinguish the importance of different data frames and are easily interfered with by non-important and irrelevant signals, thus causing a reduction in fault diagnosis accuracy, a wind turbine gearbox fault prediction model based on the attention-weighted long short-term memory network (AW-LSTM) is proposed. Specifically, the gearbox vibration signal is decomposed by empirical modal decomposition (EMD), to contain seven different frequency components and one residual component. The decomposed signal is passed through a four-layer LSTM network, to extract the fault features. The attention mechanism is introduced, to reweight the hidden states, in order to strengthen the attention to the important features. The proposed method captures the intrinsic long-term temporal correlation of timing gearbox signals through a long short-term memory network, and resorts to recursive attentional weighting, to efficiently distinguish the contribution of different frames and to exclude the influence of irrelevant or interfering data on the model. The results show that the proposed AW-LSTM wind turbine gearbox fault prediction model has an inference time of 36 s on two publicly available wind turbine fault detection datasets, with a root mean square error of 1.384, an average absolute error of 0.983, and an average absolute percentage error of 9.638, and that the AW-LSTM prediction model is able to efficiently extract the characteristics of wind turbine gearbox faults, with a shorter inference time and better fault prediction.

A Jeap-BiLSTM Neural Network for Action Recognition

Human action recognition in videos is an important task in computer vision with applications in fields such as surveillance, human–computer interaction, and sports analysis. However, it is a challenging task due to the complex background changes and redundancy of long-term video information. In this paper, we propose a novel bi-directional long short-term memory method with attention pooling based on joint motion and difference entropy (JEAP-BiLSTM) to address these challenges. To obtain discriminative features, we introduce a joint entropy map that measures both the entropy of motion and the entropy of change. The Bi-LSTM method is then applied to capture visual and temporal associations in both forward and backward directions, enabling efficient capture of long-term temporal correlation. Furthermore, attention pooling is used to highlight the region of interest and to mitigate the effects of background changes in video information. Experiments on the UCF101 and HMDB51 datasets demonstrate that the proposed JEAP-BiLSTM method achieves recognition rates of 96.4% and 75.2%, respectively, outperforming existing methods. Our proposed method makes significant contributions to the field of human action recognition by effectively capturing both spatial and temporal patterns in videos, addressing background changes, and achieving state-of-the-art performance.

Slow-Varying Dynamics-Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation.

Capsule network (CapsNet) acts as a promising alternative to the typical convolutional neural network, which is the dominant network to develop the remaining useful life (RUL) estimation models for mechanical equipment. Although CapsNet comes with an impressive ability to represent entities' hierarchical relationships through a high-dimensional vector embedding, it fails to capture the long-term temporal correlation of run-to-failure time series measured from degraded mechanical equipment. On the other hand, the slow-varying dynamics, which reveals the low-frequency information hidden in mechanical dynamical behavior, is overlooked in the existing RUL estimation models (including CapsNet), limiting the utmost ability of advanced networks. To address the aforementioned concerns, we propose a slow-varying dynamics-assisted temporal CapsNet (SD-TemCapsNet) to simultaneously learn the slow-varying dynamics and temporal dynamics from measurements for accurate RUL estimation. First, in light of the sensitivity of fault evolution, slow-varying features are decomposed from normal raw data to convey the low-frequency components corresponding to the system dynamics. Next, the long short-term memory (LSTM) mechanism is introduced into CapsNet to capture the temporal correlation of time series. To this end, experiments conducted on an aircraft engine and a milling machine verify that the proposed SD-TemCapsNet outperforms the mainstream methods. In comparison with CapsNet, the estimation accuracy of the aircraft engine with four different scenarios has been improved by 10.17%, 24.97%, 3.25%, and 13.03% about the index root mean squared error, respectively. Similarly, the estimation accuracy of the milling machine has been improved by 23.57% compared to LSTM and 19.54% compared to CapsNet.

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.

Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.

STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction

The drastic increase of metro passengers in recent years inevitably causes the overcrowdedness in the metro systems. Accurately predicting passenger flows at metro stations is critical for efficient metro system management, which helps alleviate such overcrowdedness. Compared to the prevalent next-step prediction, multi-step passenger flow prediction could prominently increase the prediction duration and reveal finer-grained passenger flow variations, which better helps metro system management. Thus, in this paper, we address the problem of <i>multi-step metro station passenger (MSP) flow prediction</i>. In light of MSP flows&#x2019; unique spatial-temporal characteristics, we propose <i>STP-TrellisNets+</i>, which for the first time augments the newly-emerged temporal convolutional framework <i>TrellisNet</i> for multi-step MSP flow prediction. The temporal module of STP-TrellisNets+ (named <i>CP-TrellisNetsED</i>) employs a Closeness TrellisNet followed by a <i>Periodicity TrellisNets-based Encoder-Decoder (P-TrellisNetsED)</i> to jointly capture the short- and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNetsED, its spatial module (named <i>GC-TrellisNetsED</i>) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements another TrellisNetsED on multiple <i>diffusion graph convolutional networks (DGCNs)</i> in time-series order to capture the dynamics of such spatial correlation. Extensive experiments with two large-scale real-world automated fare collection datasets demonstrate that STP-TrellisNets+ outperforms the state-of-the-art baselines.

Reconstructing Clear Image for High-Speed Motion Scene With a Retina-Inspired Spike Camera

Conventional digital cameras typically accumulate all the photons within an exposure period to form a snapshot image. It requires the scene to be quite still during the imaging time, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scenes. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic light intensity variation continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, which fires a spike and restarts the photon accumulation immediately once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from captured spike stream, this paper presents an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via adaptive temporal filtering. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with low model complexity. Experimental results demonstrate that the proposed scheme outperforms the existing schemes significantly, producing much better objective and subjective qualities for spike camera image reconstruction.

Chaotic and multifractal characteristic analysis of noise of thermal variables from rotary kiln

In numbers of industrial fields, many filtering algorithms of industrial signals, mechanism-based modeling methods and control strategies are based on the hypothesis of white noise. However, some researchers propose that the colored noise is closer to the real noise than the white noise. Then, whether the noise is the white noise, the colored noise or other else? And what is the intrinsic dynamic characteristics of the noise? In this paper, noise signals of thermal variables from rotary kiln are extracted and their chaotic, statistical and multifractal characteristics are analyzed to answer the two questions. Based on the experimental results, it is the first time to discover that they are not the white noise or the monofractal colored noise but have the high-dimensional chaotic characteristic, that is, they are determinate and predictable for short term theoretically. However, some models are failed to predict them. Then, further experimental results imply those noise signals have both persistent and anti-persistent multifractal characteristics. In particular, the latter is a reason to failed predictions of noise signals. Moreover, it is firstly discovered that the multifractality of each noise signal is generated mainly by the long-term temporal correlation. Finally, two ideas about modeling multifractality of noise from rotary kiln are proposed as the future work.

A Mnemonic Kalman Filter for Non-Linear Systems With Extensive Temporal Dependencies

Analytic dynamic models for target estimation are often approximations of the potentially complex behaviour of the object of interest. Its true motion might depend on hundreds of parameters and can involve long-term temporal correlation. However, conventional models keep the degrees of freedom low and they usually assume the Markov property to reduce computational complexity. In particular, the Kalman Filter assumes prior and posterior Gaussian densities and is hence restricted to linear transition functions which are often insufficient to reflect the behaviour of a real object. In this letter, a Mnemonic Kalman Filter is introduced which overcomes the Markov property and the linearity restriction by learning to predict a full transition probability density with Long Short-Term Memory networks.

Nonlinear Hybrid Precoding for Coordinated Multi-Cell Massive MIMO Systems

This paper examines nonlinear hybrid precoding with minimum mean square error (MMSE)-vector perturbation (VP) for multi-cell massive multiple-input multiple-output (MIMO) systems. Two-timescale channel state information (CSI) is assumed, which consists of short-term noisy observations of the RF-precoded MIMO channel, and perfect knowledge of the long-term channel temporal and spatial correlation. By exploiting the low-dimensional effective CSI, we propose to estimate the instantaneous realization of the high-dimensional CSI via Kalman filtering. The CSI estimate is then utilized for RF precoding in consideration of centralized and distributed MMSE-VP at baseband. By abstracting the effect of nonlinear baseband precoding, RF precoding is separately formulated as a solution to balance the error performance of signal detection with the accuracy of channel tracking. To solve such nonconvex problems, we develop Cayley transformation-based gradient descent algorithms. Numerical results demonstrate the benefits of incorporating CSI tracking into hybrid precoding from its superior bit error rate to other transmit spatial correlation-based baselines, and its improved resilience to the channel estimation errors over the fully digital counterpart.

Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots

The latest video coding standard High Efficiency Video Coding (HEVC) can achieve much higher coding efficiency than previous video coding standards. Particularly, by exploiting the hierarchical B-picture prediction structure, temporal redundancy among neighbor frames is eliminated remarkably well. In practice, videos available to consumers usually contain many repeated shots, such as TV series, movies, and talk shows. According to our observations, when these videos are encoded by HEVC with the hierarchical B-picture structure, the temporal correlation in each shot is well exploited. However, the long-term correlation between repeated shots has not been used. We propose a long-term prediction (LTP) scheme to use the long-term temporal correlation between correlated shots in a video. The long-term reference (LTR) frames of a source video are chosen by clustering similar shots and extracting the representative frames, and a modified hierarchical B-picture coding structure based on an LTR frame is introduced to support long-term temporal prediction. An adaptive quantization method is further designed for LTR frames to improve the overall video coding efficiency. Experimental results show that up to 22.86% coding gain can be achieved using the new coding scheme.

Block-Composed Background Reference for High Efficiency Video Coding

A block-composed background reference method is proposed in this paper for High Efficiency Video Coding (HEVC). For a group of picture (GoP), the first reconstructed picture is served as an initial background reference, which probably includes foreground content. In the subsequent coding, some background coding tree units (CTUs) in every picture are selected to be compressed with high quality. These reconstructed CTUs are used to update the background reference as well as replace the foreground content. Finally, a high-quality background reference is generated to better exploit the long-term temporal correlation in the video. There are three key technical contributions in the proposed coding scheme. First, the background reference is generated gradually by block updating instead of picture updating, which makes the scheme free of bit-rate burst and more suitable for real-time applications and can generate high-quality background reference even with complicated foreground. Second, we propose an approach to select background CTUs by taking both temporal and spatial smoothness into account. Third, we propose a model to decide the coding parameters of the selected background CTUs based on the overall picture activity, which essentially pursues the GoP-level optimal performance when making CTU-level decision. The proposed background reference is implemented into HEVC, and the experimental results demonstrate a significant improvement in coding efficiency. Compared with HEVC, our method can averagely save 14% bits in surveillance and conferencing sequences with negligible increase of encoding and decoding complexity. In particular, it can still averagely save 7.3% bits in HEVC general test sequences. Obviously, the proposed scheme can be applied to more general video contents.

Scene-library-based video coding scheme exploiting long-term temporal correlation

In movies and TV shows, it is common that several scenes repeat alternately. These videos are characterized with the long-term temporal correlation, which can be exploited to improve video coding efficiency. However, in applications supporting random access (RA), a video is typically divided into a number of RA segments (RASs) by RA points (RAPs), and different RASs are coded independently. In such a way, the long-term temporal correlation among RASs with similar scenes cannot be used. We present a scene-library-based video coding scheme for the coding of videos with repeated scenes. First, a compact scene library is built by clustering similar scenes and extracting representative frames in encoding video. Then, the video is coded using a layered scene-library-based coding structure, in which the library frames serve as long-term reference frames. The scene library is not cleared by RAPs so that the long-term temporal correlation between RASs from similar scenes can be exploited. Furthermore, the RAP frames are coded as interframes by only referencing library frames so as to improve coding efficiency while maintaining RA property. Experimental results show that the coding scheme can achieve significant coding gain over state-of-the-art methods.

Sea clutter transient spatial coherence and scan-to-scan constant false alarm rate

High-resolution radar sea clutter exhibits long-term temporal and spatial correlation features consistent with the sea surface wave structure. Data are presented showing that there is a transient coherence in the long-term fluctuations of the clutter, which locally appears to show the single periodic fluctuation of a dominant wave, but when averaged over a large area is seen to contain a wide band of fluctuation frequencies. This apparently non-stationary behaviour makes it difficult to predict the local sea clutter level for constant false alarm rate (CFAR) thresholding using conventional techniques. A new method of scan-to-scan CFAR processing is therefore proposed, which takes account of this behaviour. The scheme is analysed and its performance characterised.

Limitations of signal averaging due to temporal correlation in laser remote-sensing measurements

Laser remote sensing involves the measurement of laser-beam transmission through the atmosphere and is subject to uncertainties caused by strong fluctuations due primarily to speckle, glint, and atmospheric-turbulence effects. These uncertainties are generally reduced by taking average values of increasing numbers of measurements. An experiment was carried out to directly measure the effect of signal averaging on back-scattered laser return signals from a diffusely reflecting target using a direct-detection differential-absorption lidar (DIAL) system. The improvement in accuracy obtained by averaging over increasing numbers of data points was found to be smaller than that predicted for independent measurements. The experimental results are shown to be in excellent agreement with a theoretical analysis which considers the effect of temporal correlation. The analysis indicates that small but long-term temporal correlation severely limits the improvement available through signal averaging.

Effect of turbulence-induced correlation on laser remote-sensing errors

Using a 10.6-micron dual-laser differential-absorption laser remote sensing system, the short- and long-term temporal correlation of the returns from topographic targets and retroreflectors are measured in order to investigate the effect of atmospheric turbulence on laser remote sensing measurement errors. The results quantify the improvement obtained through use of a dual-laser system compared to that of a single-laser system and are in good agreement with those predicted by a theory which considers the effect of partial temporal correlation on the resultant measurement error.