Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
In order to improve the resolution and accuracy of seismic inversion, this study constructs a multi-scale super-asymmetric network (Cycle-JNet). In this model, wavelet analysis is used to capture the multi-scale data characteristics of well-seismic data, thereby improving the machine’s ability to learn details. Using the UNet neural network from Convolutional Neural Network (CNN), we modified the network structure by adding several convolution kernel layers at the output end to expand generated data, solving the problem of mismatched resolutions in well-seismic data, thus improving the resolution of seismic inversion and achieving the purpose of accurately identifying thin sandstone layers. Meanwhile, a cycle structure of Recurrent Neural Network (RNN) was designed for the secondary learning of the seismic data generated by JNet. By comparing the data transformed through inverse wavelet transform with the original data again, the accuracy of machine learning can be improved. After optimization, the Cycle-JNet model significantly outperforms traditional seismic inversion methods in terms of resolution and accuracy. This indicates that this method can provide more precise inversion results in more complex data environments, providing stronger support for seismic analysis....
Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems....
Aim. Automatic processing of the data in order to determine the status of work and identification of the activity and brain-wave frequencies becomes necessary for the modern systems in the in the diagnosis of biofeedback among athletes. Concept. The study aimed to explore the effects of physical exertion on alterations in the manifestation of brain wave frequencies (pre/post exercises) in a group of 15 endurance athletes. Results and conclusion. Statistic methods allowed an identification of data anomalies, such as extreme, outliers and missing values. Combining information with soft computing tool can distinguish the level of electrical activity of the analysed muscles. Used Big Data and Data Mining tools solution with a statistical approach while maintaining high measurement accuracy indicates the effectiveness of this method in medical diagnosis....
Heterogeneous image features are complementary, and feature fusion of heterogeneous images can increase position effectiveness of occluded apple targets. A YOLOfuse apple detection model based on RGB-D heterogeneous image feature fusion is proposed. Combining the CSPDarknet53-Tiny network on the basis of a YOLOv5s backbone network, a two-branch feature extraction network is formed for the extraction task of RGB-D heterogeneous images. The two-branch backbone network is fused to maximize the retention of useful features and reduce the computational effort. A coordinate attention (CA) module is embedded into the backbone network. The Soft-NMS algorithm is introduced, instead of the general NMS algorithm, to reduce the false suppression phenomenon of the algorithm on dense objects and reduce the missed position rate of obscured apples. It indicates that the YOLOfuse model has an AP value of 94.2% and a detection frame rate of 51.761 FPS. Comparing with the YOLOv5 s, m, l, and x4 versions as well as the YOLOv3, YOLOv4, YOLOv4-Tiny, and Faster RCNN on the test set, the results show that the AP value of the proposed model is 0.8, 2.4, 2.5, 2.3, and 2.2 percentage points higher than that of YOLOv5s, YOLOv3, YOLOv4, YOLOv4-Tiny, and Faster RCNN, respectively. Compared with YOLOv5m, YOLOv5l, and YOLOv5x, the speedups of 9.934FPS, 18.45FPS, and 23.159FPS are obtained in the detection frame rate, respectively, and the model are better in both of parameter’s number and model size. The YOLOfuse model can effectively fuse RGB-D heterogeneous source image features to efficiently identify apple objects in a natural orchard environment and provide technical support for the vision system of picking robots....
This study focuses on addressing the challenges associated with labor-intensive soil penetration resistance (SPR) measurements, which are prone to errors due to varying soil moisture levels. The innovative approach involves developing SPR estimation models using artificial neural networks (ANN) for soils with optimal moisture levels determined by van Genuchten (WG) calculations. Sampling and measurements were conducted at 280 points (0–30 cm depth), with an additional 324 samples used for model testing. Considering six scenarios, this study aimed to identify the best estimation model using key soil properties (sand, clay, silt, bulk density, organic carbon, and aggregate stability) in different combinations affecting SPR. Results from all ANN scenarios demonstrated satisfactory SPR estimation performance, with the sand and clay content scenario exhibiting the highest accuracy, characterized by a mean square error (MSE) of 0.0029 and a coefficient of determination (R2) value of 0.9707. This selected scenario were further validated with different test data, yielding an MSE of 0.7891 and an R2 value of 0.67. In conclusion, this study suggests that, by standardizing moisture levels through WG calculations, ANN-based SPR estimation can effectively be applied to soils with specific sand and clay contents....
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