目的探讨戴耳机听音乐对噪声作业工人高频噪声性听力损失(NIHL)的影响。方法采用判断抽样方法,以某汽车制造厂651名男性噪声作业工人为研究对象,对其进行个体噪声接触水平和纯音听阈测试;根据研究对象下班后戴耳机听音乐的频率分为低、...目的探讨戴耳机听音乐对噪声作业工人高频噪声性听力损失(NIHL)的影响。方法采用判断抽样方法,以某汽车制造厂651名男性噪声作业工人为研究对象,对其进行个体噪声接触水平和纯音听阈测试;根据研究对象下班后戴耳机听音乐的频率分为低、中和高频率使用耳机组,分别有60、436、155人。分析戴耳机听音乐联合职业性噪声接触对高频NIHL的影响。结果研究对象高频NIHL检出率为31.3%(204/651)。3组人群高频NIHL检出率由低到高依次为低、中和高频率使用耳机组(P<0.01);高频率使用耳机组人群高频NIHL检出率分别高于低和中频率使用耳机组(43.2% vs 25.0%,43.2% vs 28.0%,P<0.01)。多因素Logistic回归分析结果显示,在排除年龄、噪声作业工龄、噪声接触水平、佩戴防噪耳塞等混杂因素的影响后,戴耳机听音乐是噪声作业工人高频NIHL的危险因素(P<0.01);戴耳机听音乐的频率越高,发生高频NIHL的风险越大。结论噪声作业工人下班后戴耳机听音乐与职业性噪声接触对其发生高频NIHL具有协同作用。展开更多
Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learnin...Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.展开更多
文摘目的探讨戴耳机听音乐对噪声作业工人高频噪声性听力损失(NIHL)的影响。方法采用判断抽样方法,以某汽车制造厂651名男性噪声作业工人为研究对象,对其进行个体噪声接触水平和纯音听阈测试;根据研究对象下班后戴耳机听音乐的频率分为低、中和高频率使用耳机组,分别有60、436、155人。分析戴耳机听音乐联合职业性噪声接触对高频NIHL的影响。结果研究对象高频NIHL检出率为31.3%(204/651)。3组人群高频NIHL检出率由低到高依次为低、中和高频率使用耳机组(P<0.01);高频率使用耳机组人群高频NIHL检出率分别高于低和中频率使用耳机组(43.2% vs 25.0%,43.2% vs 28.0%,P<0.01)。多因素Logistic回归分析结果显示,在排除年龄、噪声作业工龄、噪声接触水平、佩戴防噪耳塞等混杂因素的影响后,戴耳机听音乐是噪声作业工人高频NIHL的危险因素(P<0.01);戴耳机听音乐的频率越高,发生高频NIHL的风险越大。结论噪声作业工人下班后戴耳机听音乐与职业性噪声接触对其发生高频NIHL具有协同作用。
基金Project supported by National Natural Science Foundation of China(51677064)State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(LAPS2016-16)National Key Research and Development Program of China(2017YFB0903902)
基金supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)fund.
文摘Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.
