With the rapid development of construction and the construction industry, the demand for mortar as a building material is also increasing. With the development of economic society, glass products have been widely used, and glass manufacturing enterprises have produced hundreds of tons of glass fragments and slag. The main component of glass is silica, which has the potential to be used as an auxiliary cementing material. Therefore, waste glass is expected to be recycled in buildings to achieve sustainability. However, due to the chemical properties of the silica tetrahedral structure stabilized by the waste glass, its pozzolanic activity is potent and needs to be stimulated. Glass powders with different degrees of fineness were obtained by physical grinding of waste glass powder (WGP). The standard consistency water consumption, compressive strength, and flexural strength of waste glass powder cement mortar were studied. The grinding times of waste glass powder are 5 min, 10 min, and 15 min, respectively, and the dosages are 5%, 10%, 15%, and 20%, respectively. The experimental results show that the average particle sizes of the grinding times of 5 min, 10 min, and 15 min are 1670.0 μm, 243.0 μm, and 13.2 μm, respectively. The waste glass powder with a grinding time of 15 min has a specific surface area of 670 m2/kg, which has high pozzolanic activity. The compressive and flexural strength of cement mortar specimens with 5% waste glass powder is the best, and the later strength is improved to a certain extent. The consistency of the cement mortar increased after adding waste glass powder. Compared with the 28 d compressive strength activity index of pure cement mortar specimens, the waste glass powder with 5–10% content reached more than 70%.
This paper presents a framework for predicting canopy states in real time by adopting a recent MATLAB based crop model: AquaCrop-OS. The historical observations are firstly used to estimate the crop sensitive parameters in Bayesian approach. Secondly, the model states will be replaced by updating remotely sensed observations in a sequential way. The final predicted states will be in comparison with the groundtruth and the RMSE of these two are 39.4155 g/ 𝒎𝟐 (calibration method) and 19.3679 g/𝒎𝟐(calibration with forcing method) concluding that the system is capable of predicting the crop status timely and improve the performance of calibration strategy.
The heat transfer at the casting-mould interface in resin-bonded sand mould casting of Mg-Gd-Y-Zr alloy was investigated, in which plate-shaped castings with different thicknesses were produced and the temperature variation in the casting and sand mould was recorded. The heat flux and the interfacial heat transfer coefficient (IHTC) were determined by a verified inverse heat conduction model. The results showed that the peak value of the heat flux was about 34~68 kW/m2 and it approximately increased with the decrease of the casting thickness. The averaged heat flux in the solidification process increased from 25.4 kW/m2 to 42.4 kW/m2 when the casting thickness decreased from 35 mm to 10 mm. The IHTC increases rapidly after the liquid metal was poured into the mould, and then decreases for a while, followed by a gentle increase. The averaged IHTC in the solidification process is about 105~183 W/m2K.
Remote sensing (RS) images enable high-resolution information collection from complex ground objects and are increasingly utilized in the earth observation research. Recently, RS technologies are continuously enhanced by various characterized platforms and sensors. Simultaneously, artificial intelligence vision algorithms are also developing vigorously and playing a significant role in RS image analysis. In particular, aiming to divide images into different ground elements with specific semantic labels, RS image segmentation could realize the visual acquisition and interpretation. As one of the pioneering methods with the advantages of deep feature extraction ability, deep learning (DL) algorithms have been exploited and proved to be highly beneficial for precise segmentation in recent years. In this paper, a comprehensive review is performed on remote sensing survey systems and various kinds of specially designed deep learning architectures. Meanwhile, DL-based segmentation methods applied on four domains are also illustrated, including geography, precision agriculture, hydrology, and environmental protection issues. In the end, the existing challenges and promising research directions in RS image segmentation are discussed. It is envisioned that this review is able to provide a comprehensive and technical reference, deployment and successful exploitation of DL empowered RS image segmentation approaches.
In traditional conveyor belt edge detection methods, contact detection methods have a high cost. At the same time noncontact detection methods have low precision, and the methods based on the convolutional neural network are limited by the local operation features of the convolution operation itself, causing problems such as insufficient perception of long-distance and global information. In order to solve the above problems, a dual flow transformer network (DFTNet) integrating global and local information is proposed for belt edge detection. DFTNet could improve belt edge detection accuracy and suppress the interference of belt image noise. In this paper, the authors have merged the advantages of the traditional convolutional neural network’s ability to extract local features and the transformer structure’s ability to perceive global and long-distance information. Here, the fusion block is designed as a dual flow encoder–decoder structure, which could better integrate global context information and avoid the disadvantages of a transformer structure pretrained on large datasets. Besides, the structure of the fusion block is designed to be flexible and adjustable. After sufficient experiments on the conveyor belt dataset, the comparative results show that DFTNet can effectively balance accuracy and efficiency and has the best overall performance on belt edge detection tasks, outperforming full convolution methods. The processing image frame rate reaches 53.07 fps, which can meet the real-time requirements of the industry. At the same time, DFTNet can deal with belt edge detection problems in various scenarios, which gives it great practical value.
Accurate Burden Surface Profile (BSP) detection is important for the operation of Blast Furnace (BF). The signal-to-noise ratio of radar signals changes greatly during both the charging period of BF and the long maintenance period of the radar device, which increases the difficulty of radar BSP detection. The traditional radar BSP detection method based on signal energy relies on manually selected detection thresholds according to the noise intensity. Hence, the accuracy of the traditional radar BSP detection method is not reliable in the long term. To address this problem, we propose a novel learning-based Key Points estimation (KP-BSP) method to detect the key points of radar reconstructed BSP image, and a new Key Points-based Connected Region Noise Reduction (KP-CRNR) algorithm to remove the noise-affected regions. The prediction deviation at detected key points (at the positions of the mechanical probes) is then used to correct the radar detection results, leading to the improvement of radar detection accuracy. The experimental data were collected from Wuhan Iron Steel Company No.7 BF. The results show that the proposed methods can achieve an average RMSE of 0.0156m, which is improved by more than 50% compared with previous methods. The long-term reliability of the proposed method is also demonstrated in this dataset.
Balanced homodyne detector (BHD) has been widely used in quantum information processing. In this paper, we design a wide bandwidth BHD and implement an optical layout to test the performance of our BHD. The test results show that the bandwidth is about 600 MHz, the quantum to classical noise ratio (QCNR) is 15.2 dB and the common mode rejection ratio (CMRR) is 41.16 dB. The performance of BHD shows that it can meet the requirements of high-speed applications in quantum information processing.
Crop disease seriously affects production because of its highly destructive property. Wheat under different levels of disease infection should be treated by various chemical strategies to enable a precision plant protection. Therefore, a fast and robust algorithm for wheat yellow rust disease severity determination is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust severity detection at leaf level. However, little reviews take field-scale rust severity detection into account by using UAV multispectral images and deep learning networks. As a result, by the means of UAV multispectral images, a real-time yellow rust detection algorithm named Efficient Dual Flow UNet (DF-UNet) to detect different levels of yellow rust is designed and proposed in this paper to meet practical requirements. First, pruning strategy is utilized to realize a lightweight structure. Second, the Sparse Channel Attention (SCA) Module is designed to increase the receptive field of the network and enhance the ability to distinguish each category. Third, by fusing SCA, a novel dual flow branch model with segmentation and ranking branch based on UNet is proposed to accomplish yellow rust severity determination at field scale. The comparative results show that the proposed method reduces more than half computation load and achieves the highest overall accuracy score among other state-of-the-art deep learning models. It is convinced that the proposed DF-UNet can pave the way for automated yellow rust severity detection at farmland scales in a robust way.
The high strength and low dielectric constant of silicon nitride (Si3N4) ceramics are an irreconcilable conflict. It is a compromise to introduce a suitable reinforcing phase in Si3N4 ceramics to receive well dielectric properties without excessive loss of mechanical properties. In this paper, Si3N4 nanowires were in-situ synthesized by pyrolysis of polysilazane with Si3N4 nanopowders as catalyst without introduction of impurities in Si3N4 porous ceramics. The formed Si3N4 nanowires not only improve the mechanical property but also reduce dielectric constant of porous Si3N4 ceramic.
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