This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and f...This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configura...This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.展开更多
目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标...目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标测算各科室床位的合理区间,得出床位调整方案。采用多层感知器神经网络模型评估床位调整方案的准确性、合理性、可行性。结果床位利用模型显示,11个(25.00%)科室属于床位效率型,11个(25.00%)科室属于床位周转型,16个(36.36%)科室属于床位闲置型,6个(13.64%)科室属于压床型。床位评价指标显示,8个科室床位数不需改变,16个科室床位数需要适当减少,20个科室床位数需要结合实际情况增加。利用多层感知器神经网络搭建床位不变、床位减少、床位增加模型。床位不变模型的受试者工作特征曲线下面积(area under curve,AUC)=0.719,灵敏度为100.00%,特异度为40.63%。床位减少模型的AUC=0.875,灵敏度为83.33%,特异度为85.00%。床位增加模型的AUC=0.913,灵敏度为100.00%,特异度为72.22%。结论医院整体床位利用效率较低且不同科室间床位的利用效率存在差异,通过多层感知器神经网络建立的床位增加模型评估结果与床位利用模型和床位评价指标的结果具有较好的一致性,能够为医院床位资源配置管理提供方法学依据,进而实现医院床位精细化管理。展开更多
文摘This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
文摘This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.
文摘目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标测算各科室床位的合理区间,得出床位调整方案。采用多层感知器神经网络模型评估床位调整方案的准确性、合理性、可行性。结果床位利用模型显示,11个(25.00%)科室属于床位效率型,11个(25.00%)科室属于床位周转型,16个(36.36%)科室属于床位闲置型,6个(13.64%)科室属于压床型。床位评价指标显示,8个科室床位数不需改变,16个科室床位数需要适当减少,20个科室床位数需要结合实际情况增加。利用多层感知器神经网络搭建床位不变、床位减少、床位增加模型。床位不变模型的受试者工作特征曲线下面积(area under curve,AUC)=0.719,灵敏度为100.00%,特异度为40.63%。床位减少模型的AUC=0.875,灵敏度为83.33%,特异度为85.00%。床位增加模型的AUC=0.913,灵敏度为100.00%,特异度为72.22%。结论医院整体床位利用效率较低且不同科室间床位的利用效率存在差异,通过多层感知器神经网络建立的床位增加模型评估结果与床位利用模型和床位评价指标的结果具有较好的一致性,能够为医院床位资源配置管理提供方法学依据,进而实现医院床位精细化管理。
