A flash bang is a non-lethal explosive device that delivers intensely loud bangs and bright lights to suppress potentially dangerous targets. It is usually used in crowd control, hostage rescue and numerous other miss...A flash bang is a non-lethal explosive device that delivers intensely loud bangs and bright lights to suppress potentially dangerous targets. It is usually used in crowd control, hostage rescue and numerous other missions. We construct a model for assessing quantitatively the risk of hearing loss injury caused by multiple flash bangs. The model provides a computational framework for incorporating the effects of the key factors defining the situation and for testing various sub-models for these factors. The proposed model includes 1) uncertainty in the burst point of flash bang mortar, 2) randomness in the dispersion of multiple submunitions after the flash bang mortar burst, 3) decay of acoustic impulse from a single submunition to an individual subject along the ground surface, 4) the effective combined sound exposure level on an individual subject caused by multiple submunitions at various distances from the subject, and 5) randomness in the spatial distribution of subjects in the crowd. With the mathematical model formulated, we seek to characterize the overall effect of flash bang mortar in the form of an effective injury area. We carry out simulations to study the effects of uncertainty and randomness on the risk of hearing loss injury of the crowd. The proposed framework serves as a starting point for a comprehensive assessment of hearing loss injury risk, taking into consideration all realistic and relevant features of flash bang mortar. It also provides a platform for testing and updating component models.展开更多
针对变压器故障识别方法在处理不均衡故障数据时存在较大偏差的问题,构建了一种基于改进轻量级梯度提升机的混合集成模型,用以变压器故障识别。首先,提出一种结合梯度调和损失函数和交叉熵损失函数的改进轻量级梯度提升机(gradient harm...针对变压器故障识别方法在处理不均衡故障数据时存在较大偏差的问题,构建了一种基于改进轻量级梯度提升机的混合集成模型,用以变压器故障识别。首先,提出一种结合梯度调和损失函数和交叉熵损失函数的改进轻量级梯度提升机(gradient harmonizing mechanism loss and cross entropy loss improved light gradient boosting machine,GCLightGBM),提升模型对数据集中少数样本的关注度。然后,针对GCLightGBM中参数特异性取值影响模型识别能力的问题,提出一种基于GCLightGBM的混合集成模型,进一步提高其准确率的同时,确保模型对现实多变不均衡数据集依然保持良好的准确率。实验结果表明,GCLightGBM可有效解决少数类样本准确率低的问题,整体准确率高达0.911。且针对其他多变不均衡数据集,基于GCLightGBM混合集成模型故障识别方法平均准确率高达0.988。展开更多
文摘A flash bang is a non-lethal explosive device that delivers intensely loud bangs and bright lights to suppress potentially dangerous targets. It is usually used in crowd control, hostage rescue and numerous other missions. We construct a model for assessing quantitatively the risk of hearing loss injury caused by multiple flash bangs. The model provides a computational framework for incorporating the effects of the key factors defining the situation and for testing various sub-models for these factors. The proposed model includes 1) uncertainty in the burst point of flash bang mortar, 2) randomness in the dispersion of multiple submunitions after the flash bang mortar burst, 3) decay of acoustic impulse from a single submunition to an individual subject along the ground surface, 4) the effective combined sound exposure level on an individual subject caused by multiple submunitions at various distances from the subject, and 5) randomness in the spatial distribution of subjects in the crowd. With the mathematical model formulated, we seek to characterize the overall effect of flash bang mortar in the form of an effective injury area. We carry out simulations to study the effects of uncertainty and randomness on the risk of hearing loss injury of the crowd. The proposed framework serves as a starting point for a comprehensive assessment of hearing loss injury risk, taking into consideration all realistic and relevant features of flash bang mortar. It also provides a platform for testing and updating component models.
文摘针对变压器故障识别方法在处理不均衡故障数据时存在较大偏差的问题,构建了一种基于改进轻量级梯度提升机的混合集成模型,用以变压器故障识别。首先,提出一种结合梯度调和损失函数和交叉熵损失函数的改进轻量级梯度提升机(gradient harmonizing mechanism loss and cross entropy loss improved light gradient boosting machine,GCLightGBM),提升模型对数据集中少数样本的关注度。然后,针对GCLightGBM中参数特异性取值影响模型识别能力的问题,提出一种基于GCLightGBM的混合集成模型,进一步提高其准确率的同时,确保模型对现实多变不均衡数据集依然保持良好的准确率。实验结果表明,GCLightGBM可有效解决少数类样本准确率低的问题,整体准确率高达0.911。且针对其他多变不均衡数据集,基于GCLightGBM混合集成模型故障识别方法平均准确率高达0.988。
