Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

Online multi-target intelligent tracking using a deep long-short term memory network

Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques

The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory（LSTM）-based models and two Convolutional Neural Network（CNN）-based models, and use these six models, Na？ve Bayes, and Support Vector Machines（SVM） to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.

Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data

The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for(sub-)surface data segmentation.Recently developed fully reversible or fully invertible networks can mostly avoid memory limitations by recomputing the states during the backward pass through the network.This results in a low and fixed memory requirement for storing network states,as opposed to the typical linear memory growth with network depth.This work focuses on a fully invertible network based on the telegraph equation.While reversibility saves the major amount of memory used in deep networks by the data,the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers.We address the explosion of the number of convolutional kernels by combining fully invertible networks with layers that contain the convolutional kernels in a compressed form directly.A second challenge is that invertible networks output a tensor the same size as its input.This property prevents the straightforward application of invertible networks to applications that map between different input-output dimensions,need to map to outputs with more channels than present in the input data,or desire outputs that decrease/increase the resolution compared to the input data.However,we show that by employing invertible networks in a non-standard fashion,we can still use them for these tasks.Examples in hyperspectral land-use classification,airborne geophysical surveying,and seismic imaging illustrate that we can input large data volumes in one chunk and do not need to work on small patches,use dimensionality reduction,or employ methods that classify a patch to a single central pixel.

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.