Multicomponent seismic data contain abundant reservoir information and have significant advantages for reservoir prediction. However, due to limited exploration in some areas, the available data samples are limited. Hence, it is important to develop new methods that can identify the complex relationship between oil-gas-bearing property and the multicomponent seismic response with limited samples. The least-squares support vector machine (LSSVM) model is suitable for solving small-sample and optimization problems. However, the parameter optimization of the LSSVM kernel function affects the prediction results. Therefore, a hybrid artificial intelligence model based on adaptive particle swarm optimization (APSO) and LSSVM was developed for multicomponent seismic reservoir prediction. Different kernel functions of the LSSVM model were analyzed to determine which one had the best predictive performance, and this initial model was used for optimization. The problem of the PSO falling into the local minimum was alleviated by adaptively adjusting the inertia weight and velocity coefficients. The kernel function parameters of the LSSVM model were globally searched via APSO, and the APSO-LSSVM model for reservoir distribution prediction was obtained. Finally, the gas-bearing probability distribution in the area with few wells was predicted using the multicomponent seismic data and the APSO-LSSVM model. The results showed that APSO had a faster convergence speed in parameter optimization than PSO. Additionally, the prediction results of the proposed hybrid artificial intelligence model (APSO-LSSVM) were more accurate than those of the LSSVM alone. This model achieved good prediction results with a small number of samples, providing a new method for gas reservoir prediction in areas with insufficient exploration degree. 

An Improved Small-Sample Method Based on APSO-LSSVM for Gas-Bearing Probability Distribution Prediction From Multicomponent Seismic Data

In the field of motion monitoring in smart home, the 5G technology can be applied to Internet of Things systems for facilitating our daily life. In this paper, a comprehensive study on the fall detection system based on 5G network is presented. Starting with analyzing the moving stability, a wearable foot pressure measurement system is devised. Furthermore, the center of pressure during moving is computed by using the pressure data. Besides, the signal transmitting security issue is also considered. The physical layer security authentication and the cross‐layer encryption are employed and integrated within the security strategy. As a case study, we evaluate the proposed method on fall detection tasks in smart home and the experimental results establish strong evidence of a satisfying performance.

A novel Internet of Things based fall detection system in smart home

Multicomponent seismic data contain abundant reservoir information and have significant advantages for reservoir prediction. However, due to limited exploration in some areas, the available data samples are limited. Hence, it is important to develop new methods that can identify the complex relationship between oil-gas-bearing property and the multicomponent seismic response with limited samples. The least-squares support vector machine (LSSVM) model is suitable for solving small-sample and optimization problems. However, the parameter optimization of the LSSVM kernel function affects the prediction results. Therefore, a hybrid artificial intelligence model based on adaptive particle swarm optimization (APSO) and LSSVM was developed for multicomponent seismic reservoir prediction. Different kernel functions of the LSSVM model were analyzed to determine which one had the best predictive performance, and this initial model was used for optimization. The problem of the PSO falling into the local minimum was alleviated by adaptively adjusting the inertia weight and velocity coefficients. The kernel function parameters of the LSSVM model were globally searched via APSO, and the APSO-LSSVM model for reservoir distribution prediction was obtained. Finally, the gas-bearing probability distribution in the area with few wells was predicted using the multicomponent seismic data and the APSO-LSSVM model. The results showed that APSO had a faster convergence speed in parameter optimization than PSO. Additionally, the prediction results of the proposed hybrid artificial intelligence model (APSO-LSSVM) were more accurate than those of the LSSVM alone. This model achieved good prediction results with a small number of samples, providing a new method for gas reservoir prediction in areas with insufficient exploration degrees.

In safety-critical automatic systems, safety can be compromised if operators lack engagement. Effective detection of undesirable engagement states can inform the design of interventions for enhancing engagement. However, the existing engagement measurement methods suffer from several limitations which damage their effectiveness in the work environment. A novel engagement evaluation methodology, which adopts Artificial Intelligence (AI) technologies, has been proposed. It was developed using motorway control room operators as subjects. Openpose and Open Source Computer Vision Library (OpenCV) were used to estimate the body postures of operators, then a Support Vector Machine (SVM) was utilized to build the engagement evaluation model based on discrete states of operator engagement. The average accuracy of the evaluation results reached 0.89 and the weighted average precision, recall and f1-score were all above 0.84. This study emphasizes the importance of specific data labelling when measuring typical engagement states, forming the basis for potential control room improvements.

A Video Processing and Machine Learning Based Method for Evaluating Safety-Critical Operator Engagement in a Motorway Control Room.

Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis

Efficient seismic phase picking is fundamental to seismic signal processing. Phase picking methods based on neural networks show great potential in accurately picking signals with a low signal-to-noise ratio but require large training datasets. We present a transductive transfer-learning-based support vector machine (TTL-SVM) algorithm for seismic phase picking when the seismic dataset possesses insufficient training samples. An objective function of TTL-SVM, which is incorporated with a pretraining classification process in the source domain that possesses an adequate training dataset and quality labeling, is proposed for phase picking in the target domain with no quality labeling. Seismic compressional (<inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>-) and shear (<inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-) phase picking is performed using two TTL-SVM processing steps: seismic phase and noise classification, and then <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-phase classification from the picking phases. Experiments are performed to test the algorithm using a simulated dataset and two earthquake datasets from Jiuzaigou in China and New Zealand. The TTL-SVM results are remarkable compared with those obtained through traditional automatic and manual picking approaches. This algorithm provides an alternative approach for seismic phase picking when the dataset possesses insufficient training samples.

Transfer-Learning-Based SVM Method for Seismic Phase Picking With Insufficient Training Samples

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Transfer-Learning-Based SVM Method for Seismic Phase Picking With Insufficient Training Samples