This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile pow...This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.展开更多
AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased cl...AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.展开更多
目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的...目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的骨导纯音听阈均≤60 dB HL)配戴韶音后挂式骨导助听器,分别于配戴助听器前和配戴第14±2 d行声场总体听阈、单音节识别率及安静环境语句识别阈测试,比较配戴助听器前后的结果差异。并于配戴第14±2 d使用IOI-HA问卷对助听器使用效果进行评估。结果 患者配戴后挂式骨导式助听器后声场四个频率平均听阈(39.3±4.9 dB HL)较配戴前(56.5±8.2 dB HL)显著改善,差异有统计学意义(P<0.001)。患者助听前单音节识别率(给声强度:患者助听前双音节言语识别阈减5 dB)为29.8%±11.4%,配戴第14±2 d为72.4%±14.4%,配戴后单音节识别率显著提高,差异有统计学意义(P<0.001)。患者语句识别阈由配戴前的48.6±9.7 dB HL降至34.3±5.6 dB HL,差异有统计学意义(P<0.001)。配戴14±2 d时IOI-HA问卷评估总分平均值为29.0±3.8分。结论 后挂式骨导助听器可显著提高传导性、0.5~4 kHz骨导纯音听阈不超过60 dB HL的混合性及感音神经性听力损失患者的听力及言语识别能力。展开更多
目的运用普通话口语识图测试(standard Chinese spoken word-picture identification test-adaptive version,MAPID-A),采用自身对照方式,探讨在不同空间噪声环境中,学龄前儿童单耳或双耳佩戴助听器时的言语识别表现。方法回顾性纳入201...目的运用普通话口语识图测试(standard Chinese spoken word-picture identification test-adaptive version,MAPID-A),采用自身对照方式,探讨在不同空间噪声环境中,学龄前儿童单耳或双耳佩戴助听器时的言语识别表现。方法回顾性纳入2018年5至2018年8月在郑州市某听力言语康复中心接受语训的母语为普通话的双侧助听器佩戴的儿童作为受试者23例,应用MAPID-A测试软件中自适应噪声下双音节词语识别测试模块,测试每位受试者在言语频谱噪声环境中的言语识别阈(accurate identification of 50%of speech in noise,SNR50)。当目标语音来自正前方,噪声分别来自前方(0°)、右侧(+90°)或左侧(-90°)时,统计分析助听器单、双耳助听器佩戴模式下儿童言语识别表现的差异。结果噪声来自前方时,单、双耳助听器佩戴模式下言语识别阈具有显著差异(M=2.38,P<0.05);噪声来自单耳佩戴助听器的同侧时,增加佩戴对侧助听器可以显著提高言语识别能力(M=4.90,P<0.05;M=5.02,P<0.05)。双耳佩戴助听器时,相比于噪声来自前方(言语和噪声空间混合),在噪声来自任一侧方(言语和噪声空间分离)的情况下,儿童没有获得显著的空间听觉掩蔽释放(spatial release from masking,SRM)(M=0.07,P>0.05;M=-0.72,P>0.05);学龄前听障儿童在噪声下言语识别能力上具有明显的个体差异。结论相比于单耳助听器佩戴,双耳助听器佩戴可以提高儿童噪声下的言语识别能力,但是对于空间噪声环境中的听觉能力改善不明显。展开更多
Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize...Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize building performance.The model structures for CAD and BEM are divergent.In this study,geometry transformation methods was implemented in BES tools for the early design stage,including auto space generation(ASG)method based on closed contour recognition(CCR)and space boundary topology calculation method.The program is developed based on modeling tools SketchUp to support the CAD format(like*.stl,*.dwg,*.ifc,etc.).It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements.In addition,this study provided a space topology calculation method based on a single-zone BEM output.The program was developed based on the SketchUp modeling tool to support additional CAD formats(such as*.stl,*.dwg,*.ifc),which can then be imported and transformed into*.obj.Compared to current methods mostly focused on BIM-BEM transformation,this method can ensure more modeling flexibility.The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program.They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions.It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.展开更多
文摘This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.
基金Supported by the Austrian Science Fund(FWF),No.KLI 429-B13 to Vécsei A
文摘AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.
文摘目的 比较韶音后挂式骨导助听器对不同类型听力损失患者的听力干预短期效果,探讨其临床应用前景。方法 55例听力损失患者(年龄18~82岁;传导性听力损失9例,感音神经性听力损失15例,混合性听力损失31例;左右耳0.5、1、2、4 kHz四个频率的骨导纯音听阈均≤60 dB HL)配戴韶音后挂式骨导助听器,分别于配戴助听器前和配戴第14±2 d行声场总体听阈、单音节识别率及安静环境语句识别阈测试,比较配戴助听器前后的结果差异。并于配戴第14±2 d使用IOI-HA问卷对助听器使用效果进行评估。结果 患者配戴后挂式骨导式助听器后声场四个频率平均听阈(39.3±4.9 dB HL)较配戴前(56.5±8.2 dB HL)显著改善,差异有统计学意义(P<0.001)。患者助听前单音节识别率(给声强度:患者助听前双音节言语识别阈减5 dB)为29.8%±11.4%,配戴第14±2 d为72.4%±14.4%,配戴后单音节识别率显著提高,差异有统计学意义(P<0.001)。患者语句识别阈由配戴前的48.6±9.7 dB HL降至34.3±5.6 dB HL,差异有统计学意义(P<0.001)。配戴14±2 d时IOI-HA问卷评估总分平均值为29.0±3.8分。结论 后挂式骨导助听器可显著提高传导性、0.5~4 kHz骨导纯音听阈不超过60 dB HL的混合性及感音神经性听力损失患者的听力及言语识别能力。
文摘目的运用普通话口语识图测试(standard Chinese spoken word-picture identification test-adaptive version,MAPID-A),采用自身对照方式,探讨在不同空间噪声环境中,学龄前儿童单耳或双耳佩戴助听器时的言语识别表现。方法回顾性纳入2018年5至2018年8月在郑州市某听力言语康复中心接受语训的母语为普通话的双侧助听器佩戴的儿童作为受试者23例,应用MAPID-A测试软件中自适应噪声下双音节词语识别测试模块,测试每位受试者在言语频谱噪声环境中的言语识别阈(accurate identification of 50%of speech in noise,SNR50)。当目标语音来自正前方,噪声分别来自前方(0°)、右侧(+90°)或左侧(-90°)时,统计分析助听器单、双耳助听器佩戴模式下儿童言语识别表现的差异。结果噪声来自前方时,单、双耳助听器佩戴模式下言语识别阈具有显著差异(M=2.38,P<0.05);噪声来自单耳佩戴助听器的同侧时,增加佩戴对侧助听器可以显著提高言语识别能力(M=4.90,P<0.05;M=5.02,P<0.05)。双耳佩戴助听器时,相比于噪声来自前方(言语和噪声空间混合),在噪声来自任一侧方(言语和噪声空间分离)的情况下,儿童没有获得显著的空间听觉掩蔽释放(spatial release from masking,SRM)(M=0.07,P>0.05;M=-0.72,P>0.05);学龄前听障儿童在噪声下言语识别能力上具有明显的个体差异。结论相比于单耳助听器佩戴,双耳助听器佩戴可以提高儿童噪声下的言语识别能力,但是对于空间噪声环境中的听觉能力改善不明显。
基金We would like to thank the National Science Foundation of China(Grant No.52130803)for funding this study.
文摘Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize building performance.The model structures for CAD and BEM are divergent.In this study,geometry transformation methods was implemented in BES tools for the early design stage,including auto space generation(ASG)method based on closed contour recognition(CCR)and space boundary topology calculation method.The program is developed based on modeling tools SketchUp to support the CAD format(like*.stl,*.dwg,*.ifc,etc.).It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements.In addition,this study provided a space topology calculation method based on a single-zone BEM output.The program was developed based on the SketchUp modeling tool to support additional CAD formats(such as*.stl,*.dwg,*.ifc),which can then be imported and transformed into*.obj.Compared to current methods mostly focused on BIM-BEM transformation,this method can ensure more modeling flexibility.The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program.They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions.It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.
