以针状焦生产工艺所产生的含酚废水为研究对象,考察不同树脂对酚类物质的吸附效果以及树脂的再生性能。实验结果表明,骨架为苯乙烯系的大孔吸附树脂对该废水中酚类物质的吸附效果最好,同时还能有效去除其中的苯胺类物质; 60 m L的树脂...以针状焦生产工艺所产生的含酚废水为研究对象,考察不同树脂对酚类物质的吸附效果以及树脂的再生性能。实验结果表明,骨架为苯乙烯系的大孔吸附树脂对该废水中酚类物质的吸附效果最好,同时还能有效去除其中的苯胺类物质; 60 m L的树脂能够处理850 m L废水,吸附后的废水达到排放标准;树脂经3次脱附-吸附实验后效果良好,可多次连续使用。这种方法工艺简单、经济高效、绿色环保,具有很好的应用前景。展开更多
Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability,...Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.展开更多
文摘以针状焦生产工艺所产生的含酚废水为研究对象,考察不同树脂对酚类物质的吸附效果以及树脂的再生性能。实验结果表明,骨架为苯乙烯系的大孔吸附树脂对该废水中酚类物质的吸附效果最好,同时还能有效去除其中的苯胺类物质; 60 m L的树脂能够处理850 m L废水,吸附后的废水达到排放标准;树脂经3次脱附-吸附实验后效果良好,可多次连续使用。这种方法工艺简单、经济高效、绿色环保,具有很好的应用前景。
基金Supported by Open Research Fund of State Key Laboratory of Advanced Technology for Vehicle Body Design & Manufacture of China (Grant No.61075002)Hunan Provincial Natural Science Foundation of China (Grant No.13JJ4033)
文摘Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.
