The Ministry of Land, Infrastructure and Transport of Korea introduced the ITS system performance evaluation about six and a half years ago. The main purpose is to make sure that accurate and reliable real-time traffi...The Ministry of Land, Infrastructure and Transport of Korea introduced the ITS system performance evaluation about six and a half years ago. The main purpose is to make sure that accurate and reliable real-time traffic data are collected from the ITS system installed. There are three types of performance evaluations, which are Quality Certification Test, Pre-Delivery Test and Periodic Check in Operation. In this paper the accuracy levels of vehicle detectors commonly used in Korea are analyzed based on the results of quality certification tests conducted during 2008-2012. The test items consist of volume, speed and occupancy. The analysis shows that loop detectors have the best levels of accuracy in all three test items and their levels of accuracy have been steady. Video image detectors do not have so good levels of accuracy as loop detectors, but the levels of accuracy have improved as time passes. Radar detectors do not have good levels of accuracy. However, their levels of accuracy have improved as time passes. The last vehicle detectors, geomagnetism detectors have the worst accuracy in the occupancy item.展开更多
Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount ...Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.展开更多
Acoustic holograms can recover wavefront stored acoustic field information and produce high-fidelity complex acoustic fields. Benefiting from the huge spatial information that traditional acoustic elements cannot matc...Acoustic holograms can recover wavefront stored acoustic field information and produce high-fidelity complex acoustic fields. Benefiting from the huge spatial information that traditional acoustic elements cannot match, acoustic holograms pursue the realization of high-resolution complex acoustic fields and gradually tend to high-frequency ultrasound applications. However, conventional continuous phase holograms are limited by three-dimensional(3D) printing size, and the presence of unavoidable small printing errors makes it difficult to achieve acoustic field reconstruction at high frequency accuracy. Here, we present an optimized discrete multi-step phase hologram. It can ensure the reconstruction quality of image with high robustness, and properly lower the requirement for the 3D printing accuracy. Meanwhile, the concept of reconstruction similarity is proposed to refine a measure of acoustic field quality. In addition, the realized complex acoustic field at 20 MHz promotes the application of acoustic holograms at high frequencies and provides a new way to generate high-fidelity acoustic fields.展开更多
Purpose To investigate the influence of different reconstruction techniques on the quantitative accuracy and image quality of PET/CT.Methods The NEMA NU2-2018 image quality phantom was scanned on a GE Discovery Elite ...Purpose To investigate the influence of different reconstruction techniques on the quantitative accuracy and image quality of PET/CT.Methods The NEMA NU2-2018 image quality phantom was scanned on a GE Discovery Elite PET/CT scanner and the spatial resolution was measured based on NEMA NU2 standard.The same raw data were reconstructed using five different algorithms:ordered subset expectation maximization(OSEM),OSEM with point spread function(PSF)modeling,OSEM with time-of-flight(TOF),OSEM with TOF and PSF,and filtered back-projection(FBP).The recovery coefficient(RC),contrast recovery coefficient(CRC),and contrast-to-noise ratio(CNR)were calculated for the six hot spheres,the percent background variability(PBV)and coefficient of variation(COV)were calculated for the background,and the residual error(RE)was calculated for lung insert in different image slices.Results The incorporation of PSF modeling showed the smallest transverse FWHM and FWTM at both 1 and 10 cm radical offsets.The combination of PSF modeling and TOF improved RCmean and CRC for all spheres and resulted in the highest ratings for the detectability of 10 mm spheres in human observer assessment.PSF modeling played a role in reducing the COV within the background region of interest and increasing the CNR of the spheres,and decreased background noise ratings in human observer assessment.Besides,TOF significantly reduced the RE in lung insert.Neither PSF modeling nor TOF had a significant effect on PBV.Compared to FBP,the OSEM algorithm showed significant advantages in PBV,COV,CNR and RE and human observer ratings of image quality,but worse results for RC_(max),RC_(mean) and CRC.Conclusions The integration of TOF and PSF modeling into the OSEM algorithm achieves improvements in both quantitative accuracy and image quality,providing distinct advantages.PSF modeling improves the spatial resolution and decreases the visual appearance of background noise.The OSEM algorithm shows significantly better image quality than the FBP algorithm but no distinct advantages concerning quantitative accuracy.展开更多
目的探讨持续质量改进在新生儿重症监护病房(neonatal intensive care unit,NICU)医护人员手卫生管理中的应用效果,以提高手卫生正确率。方法2022年12月—2023年11月应用PDCA循环管理方法对39名医护人员手卫生的正确性进行持续质量改进...目的探讨持续质量改进在新生儿重症监护病房(neonatal intensive care unit,NICU)医护人员手卫生管理中的应用效果,以提高手卫生正确率。方法2022年12月—2023年11月应用PDCA循环管理方法对39名医护人员手卫生的正确性进行持续质量改进,通过制订计划、对策实施、检查与处理4个阶段,及时发现管理中的薄弱环节,不断总结持续改进。对实施质量改进措施前后的手卫生正确率情况进行统计学分析。结果PDCA循环实施后,医护人员手卫生正确率从49.79%提高到95.15%,干预前后手卫生正确率比较,差异有统计学意义(P<0.01);同时正确率在不同指征层面上较实施前均有明显提高。结论应用PDCA循环进行持续质量改进,可以切实有效提高医务人员手卫生正确率。展开更多
针对目前在混合现实(MR)环境中高效率建立高质量三维(3D)模型的需求,基于神经辐射场算法(NeRF)的三维重建技术,提出了一种基于Laplacian算子的数据集优化算法。首先,围绕某线切割设备录制了一段1 min 51 s的视频,并采取等距提取视频帧...针对目前在混合现实(MR)环境中高效率建立高质量三维(3D)模型的需求,基于神经辐射场算法(NeRF)的三维重建技术,提出了一种基于Laplacian算子的数据集优化算法。首先,围绕某线切割设备录制了一段1 min 51 s的视频,并采取等距提取视频帧的方式,获取了训练数据集;然后,使用Laplacian算子对数据集进行了优化,同时保留了原始数据集作为对比,使用了基于NeRF算法的重建方式与传统的基于COLMAP的稠密点云重建方式,分别对两组数据集进行了三维重建;最后,在重建精度与重建速度方面,对不同重建方式、不同重建数据集的重建结果进行了比较。研究结果表明:COLMAP稠密点云重建耗时是基于NeRF重建耗时的9.98倍,而相较于COLMAP稠密点云重建,使用NeRF重建方式的模型表面缺陷较少;此外,使用Laplacian算子优化的数据集的NeRF重建在峰值信噪比(PSNR)和结构相似性(SSIM)指标上分别提升了2.43%、0.72%,有利于提升重建模型的质量。研究结果支持混合现实技术在制造业数字化转型中的应用,可为其提供有益的参考。展开更多
基金‘Development of the Universal Portable Reference Equipment for the Efficient ITS System Performance Evaluation’under The Strategic Basic Research Program of the Korea Institute of Construction Technology.
文摘The Ministry of Land, Infrastructure and Transport of Korea introduced the ITS system performance evaluation about six and a half years ago. The main purpose is to make sure that accurate and reliable real-time traffic data are collected from the ITS system installed. There are three types of performance evaluations, which are Quality Certification Test, Pre-Delivery Test and Periodic Check in Operation. In this paper the accuracy levels of vehicle detectors commonly used in Korea are analyzed based on the results of quality certification tests conducted during 2008-2012. The test items consist of volume, speed and occupancy. The analysis shows that loop detectors have the best levels of accuracy in all three test items and their levels of accuracy have been steady. Video image detectors do not have so good levels of accuracy as loop detectors, but the levels of accuracy have improved as time passes. Radar detectors do not have good levels of accuracy. However, their levels of accuracy have improved as time passes. The last vehicle detectors, geomagnetism detectors have the worst accuracy in the occupancy item.
文摘Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.
基金Project supported by the China Postdoctoral Science Foundation (Grant No.2023M732745)the National Natural Science Foundations of China (Grant Nos.61974110 and 62104177)+1 种基金the Fundamental Research Funds for the Central Universities,China (Grant Nos.QTZX23022 and JBF211103)the Cooperation Program of XDU– Chongqing IC Innovation Research Institute (Grant No.CQ IRI-2022CXY-Z07)。
文摘Acoustic holograms can recover wavefront stored acoustic field information and produce high-fidelity complex acoustic fields. Benefiting from the huge spatial information that traditional acoustic elements cannot match, acoustic holograms pursue the realization of high-resolution complex acoustic fields and gradually tend to high-frequency ultrasound applications. However, conventional continuous phase holograms are limited by three-dimensional(3D) printing size, and the presence of unavoidable small printing errors makes it difficult to achieve acoustic field reconstruction at high frequency accuracy. Here, we present an optimized discrete multi-step phase hologram. It can ensure the reconstruction quality of image with high robustness, and properly lower the requirement for the 3D printing accuracy. Meanwhile, the concept of reconstruction similarity is proposed to refine a measure of acoustic field quality. In addition, the realized complex acoustic field at 20 MHz promotes the application of acoustic holograms at high frequencies and provides a new way to generate high-fidelity acoustic fields.
文摘Purpose To investigate the influence of different reconstruction techniques on the quantitative accuracy and image quality of PET/CT.Methods The NEMA NU2-2018 image quality phantom was scanned on a GE Discovery Elite PET/CT scanner and the spatial resolution was measured based on NEMA NU2 standard.The same raw data were reconstructed using five different algorithms:ordered subset expectation maximization(OSEM),OSEM with point spread function(PSF)modeling,OSEM with time-of-flight(TOF),OSEM with TOF and PSF,and filtered back-projection(FBP).The recovery coefficient(RC),contrast recovery coefficient(CRC),and contrast-to-noise ratio(CNR)were calculated for the six hot spheres,the percent background variability(PBV)and coefficient of variation(COV)were calculated for the background,and the residual error(RE)was calculated for lung insert in different image slices.Results The incorporation of PSF modeling showed the smallest transverse FWHM and FWTM at both 1 and 10 cm radical offsets.The combination of PSF modeling and TOF improved RCmean and CRC for all spheres and resulted in the highest ratings for the detectability of 10 mm spheres in human observer assessment.PSF modeling played a role in reducing the COV within the background region of interest and increasing the CNR of the spheres,and decreased background noise ratings in human observer assessment.Besides,TOF significantly reduced the RE in lung insert.Neither PSF modeling nor TOF had a significant effect on PBV.Compared to FBP,the OSEM algorithm showed significant advantages in PBV,COV,CNR and RE and human observer ratings of image quality,but worse results for RC_(max),RC_(mean) and CRC.Conclusions The integration of TOF and PSF modeling into the OSEM algorithm achieves improvements in both quantitative accuracy and image quality,providing distinct advantages.PSF modeling improves the spatial resolution and decreases the visual appearance of background noise.The OSEM algorithm shows significantly better image quality than the FBP algorithm but no distinct advantages concerning quantitative accuracy.
文摘目的探讨持续质量改进在新生儿重症监护病房(neonatal intensive care unit,NICU)医护人员手卫生管理中的应用效果,以提高手卫生正确率。方法2022年12月—2023年11月应用PDCA循环管理方法对39名医护人员手卫生的正确性进行持续质量改进,通过制订计划、对策实施、检查与处理4个阶段,及时发现管理中的薄弱环节,不断总结持续改进。对实施质量改进措施前后的手卫生正确率情况进行统计学分析。结果PDCA循环实施后,医护人员手卫生正确率从49.79%提高到95.15%,干预前后手卫生正确率比较,差异有统计学意义(P<0.01);同时正确率在不同指征层面上较实施前均有明显提高。结论应用PDCA循环进行持续质量改进,可以切实有效提高医务人员手卫生正确率。
文摘针对目前在混合现实(MR)环境中高效率建立高质量三维(3D)模型的需求,基于神经辐射场算法(NeRF)的三维重建技术,提出了一种基于Laplacian算子的数据集优化算法。首先,围绕某线切割设备录制了一段1 min 51 s的视频,并采取等距提取视频帧的方式,获取了训练数据集;然后,使用Laplacian算子对数据集进行了优化,同时保留了原始数据集作为对比,使用了基于NeRF算法的重建方式与传统的基于COLMAP的稠密点云重建方式,分别对两组数据集进行了三维重建;最后,在重建精度与重建速度方面,对不同重建方式、不同重建数据集的重建结果进行了比较。研究结果表明:COLMAP稠密点云重建耗时是基于NeRF重建耗时的9.98倍,而相较于COLMAP稠密点云重建,使用NeRF重建方式的模型表面缺陷较少;此外,使用Laplacian算子优化的数据集的NeRF重建在峰值信噪比(PSNR)和结构相似性(SSIM)指标上分别提升了2.43%、0.72%,有利于提升重建模型的质量。研究结果支持混合现实技术在制造业数字化转型中的应用,可为其提供有益的参考。
