The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective s...The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.展开更多
在进行多雷达多目标航迹匹配时,由于测元系统误差随跟踪距离传播,雷达测量精度下降,易出现航迹匹配错误的情况。针对该情况,应用多测元非线性融合模型,采用样条约束的误差模型最佳弹道估计(Error Model Best Estimation of Trajectory,E...在进行多雷达多目标航迹匹配时,由于测元系统误差随跟踪距离传播,雷达测量精度下降,易出现航迹匹配错误的情况。针对该情况,应用多测元非线性融合模型,采用样条约束的误差模型最佳弹道估计(Error Model Best Estimation of Trajectory,EMBET)方法对雷达测元系统误差进行自校准,将利用欧几里得距离进行航迹匹配的传统方法改为利用精度较高的雷达测距作差比较,有效解决了多雷达航迹匹配时的门限阈值合理设置的难题。仿真结果证明算法有效适用,可极大地提高多雷达多目标航迹匹配时的准确度,对完成多目标空域分布、目标识别等突防效果分析评估具有重要价值。展开更多
Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology...Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.展开更多
基金supported by the National Natural Science Foundation of China(No.52174021)Key Research and Develop-ment Project of Hainan Province(No.ZDYF2022GXJS 003).
文摘The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.
文摘在进行多雷达多目标航迹匹配时,由于测元系统误差随跟踪距离传播,雷达测量精度下降,易出现航迹匹配错误的情况。针对该情况,应用多测元非线性融合模型,采用样条约束的误差模型最佳弹道估计(Error Model Best Estimation of Trajectory,EMBET)方法对雷达测元系统误差进行自校准,将利用欧几里得距离进行航迹匹配的传统方法改为利用精度较高的雷达测距作差比较,有效解决了多雷达航迹匹配时的门限阈值合理设置的难题。仿真结果证明算法有效适用,可极大地提高多雷达多目标航迹匹配时的准确度,对完成多目标空域分布、目标识别等突防效果分析评估具有重要价值。
文摘Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.
