Compaction Density Evaluation Model of Sand-Gravel Dam Based on Elman Neural Network With Modified Particle Swarm Optimization

The compaction density of sand-gravel materials has a strong gradation correlation, mainly affected by some material source parameters such as P5 content (material proportion with particle size greater than 5 mm), maximum particle size and curvature coefficient. When evaluating the compaction density of sand-gravel materials, the existing compaction density evaluation models have poor robustness and adaptability because they do not take into full consideration the impact of material source parameters. To overcome the shortcomings of existing compaction density models, this study comprehensively considers the impact of material source parameters and compaction parameters on compaction density. Firstly, asymmetric data were fused and a multi-source heterogeneous dataset was established for compaction density analysis. Then, the Elman neural network optimized by the adaptive simulated annealing particle swarm optimization algorithm was proposed to establish the compaction density evaluation model. Finally, a case study of the Dashimen water conservancy project in China is employed to demonstrate the effectiveness and feasibility of the proposed method. The results show that this model performs high-precision evaluation of the compaction density at any position of the entire working area which can timely correct the weak area of compaction density on the spot, and reduce the number of test pit tests.

Evaluation of pin penetration probing technique for the assessment of basic density in air-dried wood

Fast and accurate evaluation of the physical and mechanical properties of engineering ‎materials is of particular importance. The in situ semi-destructive and non-destructive tests versus the ‎static tests for determining the time-consuming physical properties have replaced many traditional ‎methods with reasonable accuracies. Determining the density as one of ‎the most important qualitative and quantitative parameters in the inspection of wood and wood-based ‎products is of great importance. For this purpose, 33 wood specimens from 11 species with varying densities were tested by pin penetration probing. Results were compared with those from ‎the basic density values from traditional methods. The results showed an exponential relationship between the pin penetration depths ‎and the basic density considering the moisture conditions but without any problems. The coefficients of ‎determination while estimating the equality of the basic density via pin penetration probing with the actual ‎basic density for both the testing specimens and the control samples were always over ‎‎0.8. Henceforth, this methodology suggested that the density evaluation could inspire higher precision than what has been achieved in previous efforts.

Tensile Strength and Density Evaluation of Composites from Waste Cotton Fabrics and High-Density Polyethylene (HDPE): Contributions to the Composite Industry and a Cleaner Environment

The growth of the textile industry and the massive use of plastic-based materials create economic growth, but it produces waste from post-use, such as clothing waste from cotton fabrics and HDPE that can be recycled and combined as composite materials. Therefore, an experiment was carried out to investigate and analyze the effect of the fiber volume fraction of waste cotton fabric (1.5%, 3.5%, 4.5%, 6%, and 7.5%) with straight fiber arrangement on the tensile strength and density. From the test results, a tensile strength of 178.4 MPa and 182.6 MPa was obtained for yield and max stress, respectively at a fiber volume fraction of 7.5%. Meanwhile, the highest density of 0.95 g/cm3 was obtained at 1.5% fiber volume fraction. The fracture macroscopic view of the specimen shows a resilience fracture (uneven and appears stringy). Although the strength of this composite cannot yet compete with the new composite material, it has a decent environmental contribution. Considering the availability of waste cotton fabrics and HDPE, it promises to be produced as a low-strength composite for construction, ornamentation, or coatings.

Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography

The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1-score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.

A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net

Weeds are one of the main factors affecting the yield and quality of agricultural products. Accurate evaluation of weed density is of great significance for field management, especially precision weeding. In this paper, a weed density calculating and mapping method in the field is proposed. An unmanned aerial vehicle (UAV) was used to capture field images. The excess green minus excess red index, combined with the minimum error threshold segmentation method, was used to segment green plants and bare land. A modified U-net was used to segment crops from images. After removing the bare land and crops from the field, images of weeds were obtained. The weed density was evaluated by the ratio of weed area to total area on the segmented image. The accuracy of the green plant segmentation was 93.5%. In terms of crop segmentation, the intersection over union (IoU) was 93.40%, and the segmentation time of a single image was 35.90 ms. Finally, the determination coefficient of the UAV evaluated weed density and the manually observed weed density was 0.94, and the root mean square error was 0.03. With the proposed method, the weed density of a field can be effectively evaluated from UAV images, hence providing critical information for precision weeding.