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In the process of dense vehicles traveling fast, there will be mutual occlusion between vehicles, which will lead to the problem of deterioration of the tracking effect of different vehicles, so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once (YOLO)v5s and deep simple online and realtime tracking (DeepSort). Given that the DeepSort algorithm is currently the most effective tracking method, this paper merges the YOLOv5 algorithm with the DeepSort algorithm. Then it adds the efficient channel attention networks (ECA-Net) focusing mechanism at the back for the cross-stage partial bottleneck with 3 convolutions (C3) modules about the YOLOv5 backbone network and before the up-sampling of the Neck feature pyramid. The YOLOv5 algorithm adopts expected intersection over union (EIOU) instead of complete intersection over union (CIOU) as the loss function of the target frame regression. The improved YOLOv5 algorithm is named YOLOv5-Block. The experimental results show that in the special vehicle target detection (TD) and tracking in the virtual simulation environment, The YOLOv5-Block algorithm has an average accuracy (AP) of 99.5%, which significantly improves the target recognition correctness for typical occlusion cases, and is 1.48 times better than the baseline algorithm. After the virtual simulation video sequence test, multiple objects tracking accuracy (MOTA) and various objects tracking precision (MOTP) improved by 10.7 and 1.75 percentage points, respectively, and the number of vehicle target identity document (ID) switches decreased. Compared with recent mainstream vehicle detection and tracking models, the YOLOv5-Block+Deepsort algorithm can accurately and continuously complete the detection and tracking tasks of special vehicle targets in different scenes. [ABSTRACT FROM AUTHOR]

An enhanced algorithm, YOLOv5s-FCW, is put forward in this study to tackle the problems that exist in the current helmet detection (HD) methods. These issues include having too many parameters, a complex network, and large computation requirements, making it unsuitable for deployment on embedded and other devices. Additionally, existing algorithms struggle with detecting small targets and do not achieve high enough recognition accuracy. Firstly, the YOLOv5s backbone network is replaced by FasterNet for feature extraction (FE), which reduces the number of parameters and computational effort in the network. Secondly, a convolutional block attention module (CBAM) is added to the YOLOv5 model to improve the detection model's ability to detect small objects such as helmets by increasing its attention to them. Finally, to enhance model convergence, the WIoU_Loss loss function is adopted instead of the GIoU_Loss loss function. As reported by the experimental results, the YOLOv5s-FCW algorithm proposed in this study has improved accuracy by 4.6% compared to the baseline algorithm. The proposed approach not only enhances detection concerning small and obscured targets but also reduces computation for the YOLOv5s model by 20%, thereby decreasing the hardware cost while maintaining a higher average accuracy regarding detection. [ABSTRACT FROM AUTHOR]

The exploitation and utilization of iron ore is the basis of the development of iron and steel industry. The process mineralogy of a middle and low grade magneitte ore in Liberia, West Africa was studied by X-ray fluorescence spectroscopy, X-ray diffraction analysis and Held emission scanning electron microscopy. The results showed that the TFe grade of the raw ore was 42.25%. The iron mainly existed in the form magnetite and pyrite. In add-on the distribution ratio of magnetite and pyrite in the iron ore was 52.92% and 4.66% respectively. Gangue minerals were mainly diopside, biotite, anorthite and so on. Magnetite existed mainly in granular, polygonal, irregular, flaky, and grid shapes. Part of magnetite grains were internally wrapped with amorphous diopside, the other part of magnetite grains were interconnected with calcium feldspar. Magnetite was distributed in the form of medium to tine particles, with particle sizes mainly distributed between 12 to 20 µm interval. Therefore, a process consisting of two stages of grinding, one stage of roughing and two stages of cleaning was adopted. An iron concentrate with TFe grade of 68.43%, TFe recovery of 91.14%, and mFe grade of 67.31% was obtained, which provides a theoretical basis for the development and utilization of the medium and low grade iron ore in West Africa. [ABSTRACT FROM AUTHOR]

Autonomous aerial refueling technology can significantly extend the operational endurance of unmanned aerial vehicles (UAVs), enhancing their ability to perform long-duration missions efficiently. In this paper, we address the identification of refueling drogues in the close docking phase of autonomous aerial refueling. We propose a high-precision real-time drogue recognition network called DREP-Net. The backbone of this network employs the DGST module for efficient feature extraction and improved representation of multi-scale information. For occlusion and complex background problems, we designed the RGConv module, which combines the re-parameterization module with the GhostNet idea to improve the detection of an occluded drogue. Meanwhile, we introduced the efficient local attention mechanism into the neck network to enhance the overall attention to the target region. Then, we designed Phead, a lightweight detection head that combines the advantages of decoupling and coupling heads to improve the detection speed. Finally, we compared our network with mainstream algorithms on a real drogue dataset, and the results show that DREP-Net has 2.7% higher mean average precision (mAP) compared to the YOLOv8n model, and the detection speed is improved by 31.4 frames per second. [ABSTRACT FROM AUTHOR]

Magnetization reduction roasting is an important method for the utilization of oolitic magnetite. In this study, the magnetization reduction behavior and kinetics of oolitic hematite in gas-based roasting were systematically investigated by X-ray photoelectron spectroscopy (XPS). The results revealed that under optimal roasting conditions of 650 °C, a roasting time of 60 min, and a CO concentration of 30%, the magnetization reduction rate of the roasted product reached 44.34%. Furthermore, the weak magnetic separation concentrate presented a TFe of 58.09% and a concentrate iron recovery of 94.3%. The results of the XPS spectrum indicated that the peak area ratio (Fe2+/Fe3+) gradually increased with an increase in roasting temperature, roasting time, and CO concentration, while over-reduction occurred when the roasting temperature exceeded 750 °C. The investigation of magnetization roasting kinetics for varying particle sizes demonstrated that the magnetization reduction process is controlled by chemical reaction, with a corresponding activation energy range of 42.96 kJ/mol to 63.29 kJ/mol, indicating the particle size has little effect on the magnetization reduction of oolitic hematite. [ABSTRACT FROM AUTHOR]

Bauxite residue is a hazardous solid waste produced in the alumina production process and has also become a significant rare earth resource. The extraction behaviors of La, Ce, Sc and Y solubilized in the phosphoric acidic leachate of bauxite residue were investigated in this study with di-(2-ethylhexyl) phosphoric acid as the extractant. With a relatively low concentration of 2% at an aqueous solution pH of 1.5, 90% Sc and 98% Y were extracted by di-(2-ethylhexyl) phosphoric acid. Less than 5% La and Ce and impurities of Fe, Al, Ti and Ca were extracted in this situation. As the concentration of di-(2-ethylhexyl) phosphoric acid increased to 20%, almost all the Sc and Y were extracted and the extraction ratios of La and Ce were 87% and 95%, respectively. A good separation of REEs against impurities was simultaneously obtained in the solvent extraction process and their separation coefficients were much higher than 1. A stepwise extraction process was proposed and established to extract Sc/Y and La/Ce sequentially from the phosphoric acidic leachate. It was further revealed that the Sc and Y in the acidic leachate were extracted by di-(2-ethylhexyl) phosphoric acid through an ion exchange process, and that the extraction of La and Ce was due to an antagonistic process where both an ion exchange reaction and a solvation reaction occurred. [ABSTRACT FROM AUTHOR]

High-iron and low-silicon red mud is not only an alkaline solid waste from Bayer process alumina production, but it is also a very important secondary iron resource. Magnetization roasting is considered as an effective and typical method for the iron recovery and removal of impurities in red mud. In this work, based on the characteristics of large specific surface area and high porosity of red mud, the kinetics of magnetization roasting and phase transformation of red mud were studied. Thermodynamic analysis results show that the reduction of iron oxide in red mud is more easily promoted by CO as reducing agent at low roasting temperature. The reduction reaction is prone to overreduction, and fayalite and ferrospinel can be formed in the reaction system. The phase transformation and iron reduction mechanism during the roasting process were evaluated. Most of hematite and goethite in the red mud decomposed in the process of magnetization roasting, released CO2, and transformed into strongly magnetic magnetite. The reaction process has some characteristics controlled by homogeneous reaction. The process of magnetization roasting reduction with CO was controlled by the hybrid control dynamics model, and the apparent activation energy was 38.31 kJ·mol−1. [ABSTRACT FROM AUTHOR]

Fe-P separation and the behaviors of Fe and P in separation process of high-phosphorus oolitic hematite ore from western Hubei province in China were studied. Various parameters, including reducing temperature and time, dosage of the pulverized coal, grinding fineness of magnetic separation feed, magnetic field intensity and grinding fineness of flotation feed were investigated. Experimental results show that the iron concentrate with total iron grade of 61.78% and phosphorus content of 0.23% was obtained from an oolitic hematite ore containing 47.71% iron and 0.874% phosphorus by magnetization roasting-magnetic separation-reverse flotation separation process under the proper conditions. In the magnetization roasting process, the Fe and P grade was rising by 1.30% and 11.49%, respectively; the forms of Fe and P are oolite hematite (magnetite) and collophanite which mainly distributed inside oolite structure and a small portion being distributed in other shapes. In the magnetic separation process, the Fe is from 48.33% to 58.52%, and P grade was slipped to 0.70% from 0.97%, however, the forms of Fe and P have no change compared with the roasted ore. In the reverse flotation separation process, Fe is from 58.52% to 61.78%, phosphorus is from 0.70% to 0.23%, the flotation concentrate mainly contained fine iron mineral and phosphorus is finely disseminated inside the iron mineral. [ABSTRACT FROM AUTHOR]