Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy i...<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images. </div>展开更多
In recent years,new preparations of traditional Chinese medicines(TCMs)have been developed,increasing the need for their clinical trials.Using placeboes rather than control drugs is increasingly popular in clinical tr...In recent years,new preparations of traditional Chinese medicines(TCMs)have been developed,increasing the need for their clinical trials.Using placeboes rather than control drugs is increasingly popular in clinical trials of TCMs,as the therapeutic effects of the tested TCMs can be more properly judged.The basic attributes of TCM placeboes include similarity,safety,applicability and controllability.In particular,it is necessary to have similarities in appearance,color,smell and taste between the tested TCMs and placeboes.This is quite difficult for some TCMs due to their distinctive smell and taste.On the other hand,according to the TCM theory on homology of medicine and food,many foods also have certain bioactivities,potentially further complicating the selection of materials for TCM placeboes.In this review,firstly,studies on the special smell and taste of TCMs were introduced.Then,the preparation quality evaluation processes for TCM placeboes were summarized and discussed,based on the relevant literature published in recent years and the research results from our own lab.This review will facilitate the further research and development of TCM placeboes.展开更多
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
文摘<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images. </div>
基金sponsored by Natural Science Foundation of Shanghai(18ZR1439800,18ZR1436600)Three-year Action Plan for the Development of Traditional Chinese Medicine of Shanghai Municipal Health Planning Commission(ZY(2018-2020)-CCCX2001-03)+2 种基金Xinglin Young Scholar Program of Shanghai University of Traditional Chinese Medicine(A1-U17205010416)the Clinical Research Fund of Shanghai Municipal Health Commission(201940296)Science and Technology Innovation Project in Traditional Chinese Medicine of Pudong New District(PDZY2021-0813)。
文摘In recent years,new preparations of traditional Chinese medicines(TCMs)have been developed,increasing the need for their clinical trials.Using placeboes rather than control drugs is increasingly popular in clinical trials of TCMs,as the therapeutic effects of the tested TCMs can be more properly judged.The basic attributes of TCM placeboes include similarity,safety,applicability and controllability.In particular,it is necessary to have similarities in appearance,color,smell and taste between the tested TCMs and placeboes.This is quite difficult for some TCMs due to their distinctive smell and taste.On the other hand,according to the TCM theory on homology of medicine and food,many foods also have certain bioactivities,potentially further complicating the selection of materials for TCM placeboes.In this review,firstly,studies on the special smell and taste of TCMs were introduced.Then,the preparation quality evaluation processes for TCM placeboes were summarized and discussed,based on the relevant literature published in recent years and the research results from our own lab.This review will facilitate the further research and development of TCM placeboes.
