This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating t...This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.展开更多
随着卫星通信技术的迅速发展,卫星互联网已成为现代通信的重要组成部分。机载卫星通信终端及其核心组件的性能直接决定了通信系统的效率与可靠性。目前,这些关键组件的研发主要由国外企业垄断,限制了技术的发展并且成本高昂。通过对比5G...随着卫星通信技术的迅速发展,卫星互联网已成为现代通信的重要组成部分。机载卫星通信终端及其核心组件的性能直接决定了通信系统的效率与可靠性。目前,这些关键组件的研发主要由国外企业垄断,限制了技术的发展并且成本高昂。通过对比5G NR NTN和DVB-S2X/RCS2两种主流卫星通信体制,总结出卫通终端的功能性能指标,并结合机载环境下的需求,分析了典型卫通终端协议模块的架构和指标。最后,研究总结了基于5G NR和DVB标准的测试指标及方法,以提高测试效能,并实现产品的实时自检应用。展开更多
目的:探讨基于年龄、体质指数、进食情况、体重减少情况的营养风险评估工具(Assessment of Nutritional Risk based on BodyMass Index,Intake and Weight loss,AIWW)和营养风险筛查2002(NRS2002)用于胃癌住院病人营养风险筛查的效果,...目的:探讨基于年龄、体质指数、进食情况、体重减少情况的营养风险评估工具(Assessment of Nutritional Risk based on BodyMass Index,Intake and Weight loss,AIWW)和营养风险筛查2002(NRS2002)用于胃癌住院病人营养风险筛查的效果,分析其在胃癌病人中的适用性。方法:采用便利抽样法,选取2023年10月—2024年4月在广西某三级甲等肿瘤医院胃及腹部肿瘤病区就诊的376例胃癌病人,于病人入院后24 h内用AIWW和NRS2002进行营养筛查,以改良版病人主观整体评估(MPG-SGA)评估结果为营养不良的诊断标准,计算AIWW和NRS2002的灵敏度、特异度、阳性预测值和阴性预测值、阳性似然比和阴性似然比、Kappa值以及受试者特征(ROC)曲线和曲线下面积。结果:共纳入376例胃癌病人,以MPG-SGA为诊断标准,254例(67.6%)病人出现营养不良;使用AIWW、NRS2002诊断胃癌病人营养不良风险分别为67.0%、34.8%。AIWW、NRS2002诊断营养不良的灵敏度分别为0.98,0.51,特异度分别为0.97,0.98;AIWW和NRS2002与MPG-SGA的Kappa一致性结果分别为0.928,0.389;ROC曲线下面积分别为0.982,0.788。结论:AIWW、NRS2002均能为胃癌病人营养不良风险筛查提供依据,且两种工具的一致性较好。诊断效能评价中,AIWW灵敏度较高,与MPG-SGA的一致性较好,且条目简单易于评估。因此,建议使用AIWW于胃癌病人的营养筛查。展开更多
基金National Natural Science Foundation of China grants no.41972326 and 51774258.
文摘This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.
文摘随着卫星通信技术的迅速发展,卫星互联网已成为现代通信的重要组成部分。机载卫星通信终端及其核心组件的性能直接决定了通信系统的效率与可靠性。目前,这些关键组件的研发主要由国外企业垄断,限制了技术的发展并且成本高昂。通过对比5G NR NTN和DVB-S2X/RCS2两种主流卫星通信体制,总结出卫通终端的功能性能指标,并结合机载环境下的需求,分析了典型卫通终端协议模块的架构和指标。最后,研究总结了基于5G NR和DVB标准的测试指标及方法,以提高测试效能,并实现产品的实时自检应用。
文摘目的:探讨基于年龄、体质指数、进食情况、体重减少情况的营养风险评估工具(Assessment of Nutritional Risk based on BodyMass Index,Intake and Weight loss,AIWW)和营养风险筛查2002(NRS2002)用于胃癌住院病人营养风险筛查的效果,分析其在胃癌病人中的适用性。方法:采用便利抽样法,选取2023年10月—2024年4月在广西某三级甲等肿瘤医院胃及腹部肿瘤病区就诊的376例胃癌病人,于病人入院后24 h内用AIWW和NRS2002进行营养筛查,以改良版病人主观整体评估(MPG-SGA)评估结果为营养不良的诊断标准,计算AIWW和NRS2002的灵敏度、特异度、阳性预测值和阴性预测值、阳性似然比和阴性似然比、Kappa值以及受试者特征(ROC)曲线和曲线下面积。结果:共纳入376例胃癌病人,以MPG-SGA为诊断标准,254例(67.6%)病人出现营养不良;使用AIWW、NRS2002诊断胃癌病人营养不良风险分别为67.0%、34.8%。AIWW、NRS2002诊断营养不良的灵敏度分别为0.98,0.51,特异度分别为0.97,0.98;AIWW和NRS2002与MPG-SGA的Kappa一致性结果分别为0.928,0.389;ROC曲线下面积分别为0.982,0.788。结论:AIWW、NRS2002均能为胃癌病人营养不良风险筛查提供依据,且两种工具的一致性较好。诊断效能评价中,AIWW灵敏度较高,与MPG-SGA的一致性较好,且条目简单易于评估。因此,建议使用AIWW于胃癌病人的营养筛查。
