目的探讨“互联网+医疗”模式在肺癌术后肺康复患者的应用效果。方法选取笔者医院2022年6月至2023年1月接受择期肺癌手术治疗的87例肺癌患者,按随机数字表法分为对照组44例与观察组43例;对照组行常规干预,观察组行基于“互联网+医疗”...目的探讨“互联网+医疗”模式在肺癌术后肺康复患者的应用效果。方法选取笔者医院2022年6月至2023年1月接受择期肺癌手术治疗的87例肺癌患者,按随机数字表法分为对照组44例与观察组43例;对照组行常规干预,观察组行基于“互联网+医疗”模式干预;对比两组肺功能指标[用力肺活量(forced vital capacity,FVC)、第1秒用力肺活量(first second forced vital capacity,FEV1)、呼吸流量(respiratory flow,PEF)]、心肺耐力水平[6min步行试验(6min walking test,6MWT)]、呼吸劳累感及生活质量[世界卫生组织生存质量测定简表(World Health Organization quality of life,WHOQOL-BREF)]。结果干预6个月,研究组FVC、FEV1、PEF、6MWT距离、WHOQOL-BREE评分均较对照组高,呼吸疲劳感评分较对照组低,差异有统计学意义(P<0.05)。结论肺癌术后康复患者采用“互联网+医疗”模式干预可改善肺功能,提高心肺耐力,减轻呼吸疲劳感,提升生活质量。展开更多
快速测量叶面积指数(Leaf Area Index,LAI)是农业、生态及环境领域野外实验研究的重要任务。针对现有测量仪器的不足,设计并实现了基于半球摄影法的便携式叶面积指数测量仪(H-LAI)。针对"叶片-背景"分割这一关键技术问题,基...快速测量叶面积指数(Leaf Area Index,LAI)是农业、生态及环境领域野外实验研究的重要任务。针对现有测量仪器的不足,设计并实现了基于半球摄影法的便携式叶面积指数测量仪(H-LAI)。针对"叶片-背景"分割这一关键技术问题,基于大津阈值算法和HSV颜色空间,构建了自适应分割算法,解决了复杂环境条件下图片自适应分割的难题。与LAI-2200测量对比结果表明,该测量仪测量结果极显著相关,R达到0.690 6(树木)、0.837 1(草地)和0.928 7(玉米)。该测量仪的研制实现了LAI准确、快速获取和存储管理,为遥感产品真实性验证、农业及生态野外试验提供了便利。展开更多
As a new type of learning machine developed on the basis of statistics learning theory, support vector machine （SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear...As a new type of learning machine developed on the basis of statistics learning theory, support vector machine （SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifter. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM（LS-SVM） and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-dassiftcation is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and twospirals to measure the performance of the classifier.展开更多
文摘目的探讨“互联网+医疗”模式在肺癌术后肺康复患者的应用效果。方法选取笔者医院2022年6月至2023年1月接受择期肺癌手术治疗的87例肺癌患者,按随机数字表法分为对照组44例与观察组43例;对照组行常规干预,观察组行基于“互联网+医疗”模式干预;对比两组肺功能指标[用力肺活量(forced vital capacity,FVC)、第1秒用力肺活量(first second forced vital capacity,FEV1)、呼吸流量(respiratory flow,PEF)]、心肺耐力水平[6min步行试验(6min walking test,6MWT)]、呼吸劳累感及生活质量[世界卫生组织生存质量测定简表(World Health Organization quality of life,WHOQOL-BREF)]。结果干预6个月,研究组FVC、FEV1、PEF、6MWT距离、WHOQOL-BREE评分均较对照组高,呼吸疲劳感评分较对照组低,差异有统计学意义(P<0.05)。结论肺癌术后康复患者采用“互联网+医疗”模式干预可改善肺功能,提高心肺耐力,减轻呼吸疲劳感,提升生活质量。
文摘As a new type of learning machine developed on the basis of statistics learning theory, support vector machine （SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifter. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM（LS-SVM） and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-dassiftcation is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and twospirals to measure the performance of the classifier.
