Compared with traditional methods, biochemical reactions at the micro-scale have faster reaction rates and lower raw material costs, and multi-site array reaction vessels can better carry out controlled experiments and repeated experiments. However, the pressure drop of the fluid under the microchannel is very large, and it is necessary to ensure a good closed space and the design of the fluid inlet and outlet. Array reactors with a high degree of integration require the designer to reasonably allocate the three-dimensional space to ensure the air tightness of the microfluidic device while increasing the reaction points of the device as much as possible. At the same time, the material of the device should be selected to meet the biocompatibility of most reagents. Herein, a microfluidic array reactor made of polytetrafluoroethylene were developed and the solution delivery process was tested to verify its stability and air tightness. This study lays the foundation for the application of microfluidics in biochemical reactions.
The accurate detection of targets on the road is of great significance (for the big data of road system). However, the traditional detection suffers from missed detection and false detection. In this work, an improved algorithm was proposed based on Yolo-v3 algorithm. The replacement of traditional K-means clustering algorithm with K-means++ algorithm endowed the system a more suitable size of detection frame. Meanwhile, the detection sensitivity of small or incomplete targets was obviously improved with the utilization of cross entropy loss function. The detection experiments on vehicles and pedestrians were performed in two actual situations, normal situation and congested situation. The average detection accuracy was increased by 2.46% with the improvement of clustering algorithm, and the accuracy was increased by 2.08% with the improvement of loss function. The average accuracy was increased by 3.56% with the combination of K-means++ algorithm and cross-entropy loss. The experiments results showed that the combined algorithm possessed a significant improvement in average accuracy, recall and detection speed. This research provides new ideas for the establishment of high-precision algorithms for road dynamic target detection.
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