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| 1 | AED-Net:An Abnormal Event Detection Network显示文摘It has long been a challenging task to detect an anomaly in a crowded scene.In this paper,a selfsupervised framework called the abnormal event detection network(AED-Net),which is composed of a principal component analysis network(PCAnet)and kernel principal component analysis(kPCA),is proposed to address this problem.Using surveillance video sequences of different scenes as raw data,the PCAnet is trained to extract high-level semantics of the crowd’s situation.Next,kPCA,a one-class classifier,is trained to identify anomalies within the scene.In contrast to some prevailing deep learning methods,this framework is completely self-supervised because it utilizes only video sequences of a normal situation.Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota(UMN dataset)and Anomaly Detection dataset from University of California,San Diego(UCSD dataset),and competitive results that yield a better equal error rate(EER)and area under curve(AUC)than other state-of-the-art methods are observed.Furthermore,by adding a local response normalization(LRN)layer,we propose an improvement to the original AED-Net.The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity. | Tian Wang Zichen Miao Yuxin Chen Yi Zhou Guangcun Shan Hichem Snoussi | 2019 | Engineering2019，5，5： | 2 |
| 2 | Effect of tip clearance on the heat transfer and pressure drop performance in the micro-reactor with micro-pin-fin arrays at low Reynolds number 显示文摘 | Mei Deqing Lou Xinyang Qian Miao Yao Zhehe Liang Lingwei Chen Zichen | 2014 | International Journal of Heat and Mass Transfer2014，70，： | 1 |
| 3 | Disease resistance conferred by components of essential chrysanthemum oil and the epigenetic regulation of OsTPS1显示文摘The sesquiterpene alpha-bisabolol is the predominant active ingredient in essential oils that are highly valued in the cosmetics industry due to its wound healing,anti-inflammatory,and skin-soothing properties.Alpha-bisabolol was thought to be restricted to Compositae plants.Here we reveal that alpha-bisabolol is also synthesized in rice,a non-Compositae plant,where it acts as a novel sesquiterpene phytoalexin.Overexpressing the gene responsible for the biosynthesis of alpha-bisabolol,Os TPS1,conferred bacterial blight resistance in rice.Phylogenomic analyses revealed that alpha-bisabolol-synthesizing enzymes in rice and Compositae evolved independently.Further experiments demonstrated that the natural variation in the disease resistance level was associated with differential transcription of Os TPS1 due to polymorphisms in its promoter.We demonstrated that Os TPS1 was regulated at the epigenetic level by JMJ705 through the methyl jasmonate pathway.These data reveal the cross-family accumulation and regulatory mechanisms of alpha-bisabolol production. | Chuansong Zhan Long Lei Hao Guo Shen Zhou Congping Xu Zixin Liu Zichen Wu Yuan Deng Yuanyuan Miao Yu Han Meng Zhang Hua Li Sishu Huang Chenkun Yang Feng Zhang Yufei Li Ling Liu Xianqing Liu Hafiz Muhammad Khalid Abbas Alisdair R.Fernie Meng Yuan Jie Luo | 2023 | Science China(Life Sciences)2023，66，5： | 1 |
| 4 | 深水支持平台火灾定量风险评估显示文摘The living area of an offshore platform is the main living place for operators in offshore oil and gas fields.Fire risk assessment plays an important role in the safety of personnel in offshore platforms.In this paper,a fire risk assessment mathematical model for offshore platfoms is proposed based on a comprehensive safety assessment method.The concept of danger time is presented according to the evaluation criteria of safe evacuation.The fire risk of offshore platforms is assessed by combining probability statistics with numerical simulation.The fire risk is quantitatively assessed by using an N500 deep water semi-submersible support platform as an example.According to the FN curve,fire frequency,fire escalation probability,and casualty probability,the rationality of marine general layout is analyzed,and the general layout design could be optimized to reduce the fire risk. | Haipeng Zhang Zichen Zhou Miao Chen Difei Yi | 2021 | Journal of Marine Science and Application2021，20，2： | 1 |
| 5 | Assessment of Atmospheric Reanalysis Data Based on Buoy Observations over the Tropical Western Indian Ocean in 2019显示文摘Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region. | LUO Yulan LIU Lin PAUL Oloo PHILIP Sagero LI Kuiping YANG Yang SU Qinglei LI Qiuchi MIAO Zichen | 2023 | Journal of Ocean University of China2023，22，4： | 0 |
| 6 | Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations显示文摘Objective and Impact Statement.We use deep learning models to classify cervix images—collected with a low-cost,portable Pocket colposcope—with biopsy-confirmed high-grade precancer and cancer.We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs,which come at no additional cost to the provider.Introduction.Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low-or middle-Human Development Indices,an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations.Methods.Our dataset consists of cervical images(n=1,760)from 880 patient visits.After optimizing the network architecture and incorporating a weighted loss function,we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set.Results.We achieve an area under the receiver-operator characteristic curve,sensitivity,and specificity of 0.87,75%,and 88%,respectively.The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity.Conclusion.Our methodology,which has already been tested on a prescreened population,can boost classification performance and,in the future,be coupled with Pap smear or HPV triaging,thereby broadening access to early detection of precursor lesions before they advance to cancer. | Erica Skerrett Zichen Miao Mercy N.Asiedu Megan Richards Brian Crouch Guillermo Sapiro Qiang Qiu Nirmala Ramanujam | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
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