Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kan...Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kangbao County Meteorological Station from 1994 to 2023 were selected,and the meteorological elements such as air pressure,temperature,precipitation,wind,relative humidity,sunshine,thunderstorm,hail,gale,rainstorm,fog,and snow cover were counted.The climate background analysis and high-impact weather analysis were carried out in combination with the topographic characteristics,geographical location,and climate characteristics.The results of meteorological sensitivity survey in the park showed that industries such as food,agriculture and new energy are very sensitive to temperature.During the visit to the enterprises in the park,it was found that heavy precipitation,snow,strong winds and hail had a great impact on many industries,and it was recommended to carry out long-term planning and reasonable design of buildings.It should pay close attention to forecasts and early warnings,formulate emergency plans for high-impact weather defense,and actively take preventive measures.展开更多
The effect of background light on the imaging quality of three typical ghost imaging(GI) lidar systems(namely narrow pulsed GI lidar, heterodyne GI lidar, and pulse-compression GI lidar via coherent detection) is inve...The effect of background light on the imaging quality of three typical ghost imaging(GI) lidar systems(namely narrow pulsed GI lidar, heterodyne GI lidar, and pulse-compression GI lidar via coherent detection) is investigated. By computing the signal-to-noise ratio(SNR) of fluctuation-correlation GI, our analytical results, which are backed up by numerical simulations, demonstrate that pulse-compression GI lidar via coherent detection has the strongest capacity against background light, whereas the reconstruction quality of narrow pulsed GI lidar is the most vulnerable to background light. The relationship between the peak SNR of the reconstruction image andσ(namely, the signal power to background power ratio) for the three GI lidar systems is also presented, and theresults accord with the curve of SNR-σ.展开更多
Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from th...Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given sequence.Although such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization ability.This situation could become worse on“long”videos since they tend to have intensive scene variations.Moreover,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model performance.Thus,the learning scheme is usually incapable of handling complex pattern modeling.To solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ones.First,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint groups.Then for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning phase.During the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing frame.Comprehensive experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.展开更多
Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challeng...Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.展开更多
文摘Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kangbao County Meteorological Station from 1994 to 2023 were selected,and the meteorological elements such as air pressure,temperature,precipitation,wind,relative humidity,sunshine,thunderstorm,hail,gale,rainstorm,fog,and snow cover were counted.The climate background analysis and high-impact weather analysis were carried out in combination with the topographic characteristics,geographical location,and climate characteristics.The results of meteorological sensitivity survey in the park showed that industries such as food,agriculture and new energy are very sensitive to temperature.During the visit to the enterprises in the park,it was found that heavy precipitation,snow,strong winds and hail had a great impact on many industries,and it was recommended to carry out long-term planning and reasonable design of buildings.It should pay close attention to forecasts and early warnings,formulate emergency plans for high-impact weather defense,and actively take preventive measures.
基金National Natural Science Foundation of China(NSFC)(61571427)Ministry of Science and Technology of the People’s Republic of China(MOST)(2013AA122901)Youth Innovation Promotion Association of the Chinese Academy of Sciences(2013162)
文摘The effect of background light on the imaging quality of three typical ghost imaging(GI) lidar systems(namely narrow pulsed GI lidar, heterodyne GI lidar, and pulse-compression GI lidar via coherent detection) is investigated. By computing the signal-to-noise ratio(SNR) of fluctuation-correlation GI, our analytical results, which are backed up by numerical simulations, demonstrate that pulse-compression GI lidar via coherent detection has the strongest capacity against background light, whereas the reconstruction quality of narrow pulsed GI lidar is the most vulnerable to background light. The relationship between the peak SNR of the reconstruction image andσ(namely, the signal power to background power ratio) for the three GI lidar systems is also presented, and theresults accord with the curve of SNR-σ.
基金supported in part by the CAMS Innovation Fund for Medical Sciences,China(No.2019-I2M5-016)National Natural Science Foundation of China(No.62172246)+1 种基金the Youth Innovation and Technology Support Plan of Colleges and Universities in Shandong Province,China(No.2021KJ062)National Science Foundation of USA(Nos.IIS-1715985 and IIS1812606).
文摘Recently,a new research trend in our video salient object detection(VSOD)research community has focused on enhancing the detection results via model self-fine-tuning using sparsely mined high-quality keyframes from the given sequence.Although such a learning scheme is generally effective,it has a critical limitation,i.e.,the model learned on sparse frames only possesses weak generalization ability.This situation could become worse on“long”videos since they tend to have intensive scene variations.Moreover,in such videos,the keyframe information from a longer time span is less relevant to the previous,which could also cause learning conflict and deteriorate the model performance.Thus,the learning scheme is usually incapable of handling complex pattern modeling.To solve this problem,we propose a divide-and-conquer framework,which can convert a complex problem domain into multiple simple ones.First,we devise a novel background consistency analysis(BCA)which effectively divides the mined frames into disjoint groups.Then for each group,we assign an individual deep model on it to capture its key attribute during the fine-tuning phase.During the testing phase,we design a model-matching strategy,which could dynamically select the best-matched model from those fine-tuned ones to handle the given testing frame.Comprehensive experiments show that our method can adapt severe background appearance variation coupling with object movement and obtain robust saliency detection compared with the previous scheme and the state-of-the-art methods.
基金Project supported by the National Natural Science Foundation of China(No.61203224)the Science and Technology Innovation Foundation of Shanghai Municipal Education Commission,China(No.13YZ101)
文摘Label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. However, satisfying the requirements of higher accuracy and less running time is always a great challenge. In this paper we propose a novel patch-based segmentation method combining a local weighted voting strategy with Bayesian inference. Multiple atlases are registered to a target image by an advanced normalization tools(ANTs) algorithm. To obtain a segmentation of the target, labels of the atlas images are propagated to the target image. We first adopt intensity prior and label prior as two key metrics when implementing the local weighted voting scheme, and then compute the two priors at the patch level. Further, we analyze the label fusion procedure concerning the image background and take the image background as an isolated label when estimating the label prior. Finally, by taking the Dice score as a criterion to quantitatively assess the accuracy of segmentations, we compare the results with those of other methods, including joint fusion, majority voting, local weighted voting, majority voting based on patch, and the widely used Free Surfer whole-brain segmentation tool. It can be clearly seen that the proposed algorithm provides better results than the other methods. During the experiments, we make explorations about the influence of different parameters(including patch size, patch area, and the number of training subjects) on segmentation accuracy.
