A ten-month field research study was meticulously conducted at Robert Moses State Park (RMSP) on the south shore of Long Island, NY. The objective was to determine if aerial phenomena of an unknown nature exist over a...A ten-month field research study was meticulously conducted at Robert Moses State Park (RMSP) on the south shore of Long Island, NY. The objective was to determine if aerial phenomena of an unknown nature exist over a coastal location and to characterize their properties and behaviors. Primary and secondary field observation methods were utilized in this data-centric study. Forensic engineering principles and methodologies guided the study. The challenges set forward were object detection, observation, and characterization, where multispectral electro-optical devices and radar were employed due to limited visual acuity and intermittent presentation of the phenomena. The primary means of detection utilized a 3 cm X-band radar operating in two scan geometries, the X- and Y-axis. Multispectral electro-optical devices were utilized as a secondary means of detection and identification. Data was emphasized using HF and LF detectors and spectrum analyzers incorporating EM, ultrasonic, magnetic, and RF field transducers to record spectral data in these domains. Data collection concentrated on characterizing VIS, NIR, SWIR, LWIR, UVA, UVB, UVC, and the higher energy spectral range of ionizing radiation (alpha, beta, gamma, and X-ray) recorded by Geiger-Müller counters as well as special purpose semiconductor diode sensors.展开更多
We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video sequence.Differ...We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video sequence.Differently from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing flows.We formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and space.Given the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine manner.For the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation performance.Comprehensive qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is demonstrated.More impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its generalizability.Our algorithm implementations are publicly available at https://github.com/zyqin19/HEVis.展开更多
Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) system...Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) systems. Moreover, a newresearch paradigm has emerged as visualizationtechniques are incorporated into these models. Thisstudy divides these intersections into two researchareas: visualization for foundation model (VIS4FM)and foundation model for visualization (FM4VIS).In terms of VIS4FM, we explore the primary roleof visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FMaddresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in termsof FM4VIS, we highlight how foundation models canbe used to advance the visualization field itself. Theintersection of foundation models with visualizations ispromising but also introduces a set of challenges. Byhighlighting these challenges and promising opportunities, this study aims to provide a starting point forthe continued exploration of this research avenue.展开更多
Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture...Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.展开更多
This paper generated gridded visibility(Vis)data from 1980 to 2018 over the South China Sea(SCS)based on artificial neural network(ANN),and the accuracy of the generated data was tested.Then,temporal and spatial chara...This paper generated gridded visibility(Vis)data from 1980 to 2018 over the South China Sea(SCS)based on artificial neural network(ANN),and the accuracy of the generated data was tested.Then,temporal and spatial characteristics of Vis in the area were analyzed based on the generated Vis data.The results showed that Vis in the southern SCS was generally better than that in the northern SCS.In the past 39 years,Vis in both spring and winter has improved,especially in winter at a significant increased speed of 0.37 km decade^(-1).However,Vis in both summer and autumn has decreased,especially in summer with an obvious reduction of 0.84 km decade^(-1).Overall,Vis is best in summer and worst in winter,averaging 31.89 km in summer and 20.96 km in winter,which may be related to the difference of climatic conditions and wind speed in different seasons.At the same time,probability of low Vis in spring is significantly higher than that in other seasons,especially in the northwest of Hainan Island and the northwest of Malaysia.展开更多
This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops.Four different nitrogen treatments of 0%,80%,100%and 120%BMP(best management practice)were studied.Pr...This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops.Four different nitrogen treatments of 0%,80%,100%and 120%BMP(best management practice)were studied.Principal component analysis-loading(PCA-loading)was used to identify the effective wavelengths.Partial least squares(PLS)and multiple linear regression(MLR)models were built to predict different nitrogen values.Vegetation indices(VIs)were calculated and then used to build more prediction models.Both full and selected wavelengths-based models showed similar prediction trends.The overall PLS model obtained the coefficient of determination(R^(2))of 0.6535 with a root mean square error(RMSE)of 0.2681 in the prediction set.The selected wavelengths for overall MLR model obtained the R^(2) of 0.6735 and RMSE of 0.3457 in the prediction set.The results showed that the wavelengths in visible and near infrared region(350-1000 nm)performed better than the two either spectral regions(1001-1350/1425-1800 nm and 2000-2400 nm).For each data set,the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates.The vogelmann red edge index 2(VOG 2)performed the best among all VIs.It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.展开更多
文摘A ten-month field research study was meticulously conducted at Robert Moses State Park (RMSP) on the south shore of Long Island, NY. The objective was to determine if aerial phenomena of an unknown nature exist over a coastal location and to characterize their properties and behaviors. Primary and secondary field observation methods were utilized in this data-centric study. Forensic engineering principles and methodologies guided the study. The challenges set forward were object detection, observation, and characterization, where multispectral electro-optical devices and radar were employed due to limited visual acuity and intermittent presentation of the phenomena. The primary means of detection utilized a 3 cm X-band radar operating in two scan geometries, the X- and Y-axis. Multispectral electro-optical devices were utilized as a secondary means of detection and identification. Data was emphasized using HF and LF detectors and spectrum analyzers incorporating EM, ultrasonic, magnetic, and RF field transducers to record spectral data in these domains. Data collection concentrated on characterizing VIS, NIR, SWIR, LWIR, UVA, UVB, UVC, and the higher energy spectral range of ionizing radiation (alpha, beta, gamma, and X-ray) recorded by Geiger-Müller counters as well as special purpose semiconductor diode sensors.
基金supported in part by the National Natural Science Foundation of China(62176139,62106128,62176141)the Major Basic Research Project of Shandong Natural Science Foundation(ZR2021ZD15)+4 种基金the Natural Science Foundation of Shandong Province(ZR2021QF001)the Young Elite Scientists Sponsorship Program by CAST(2021QNRC001)the Open Project of Key Laboratory of Artificial Intelligence,Ministry of Educationthe Shandong Provincial Natural Science Foundation for Distinguished Young Scholars(ZR2021JQ26)the Taishan Scholar Project of Shandong Province(tsqn202103088)。
文摘We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect,segment and track each instance in a video sequence.Differently from current discriminative tracking-by-detection solutions,our proposed hierarchical structural embedding learning can predict more highquality masks with accurate boundary details over spatio-temporal space via the normalizing flows.We formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and space.Given the video clip,our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine manner.For the mixing distribution,we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation performance.Comprehensive qualitative,quantitative,and ablation experiments are performed on three representative video instance segmentation benchmarks(i.e.,YouTube-VIS19,YouTube-VIS21,and OVIS)and the effectiveness of the proposed method is demonstrated.More impressively,the superior performance of our model on an unsupervised video object segmentation dataset(i.e.,DAVIS19)proves its generalizability.Our algorithm implementations are publicly available at https://github.com/zyqin19/HEVis.
基金supported by the National Natural Science Foundation of China(Grant Nos.U21A20469 and 61936002)the National Key R&D Program of China(Grant No.2020YFB2104100)grants from the Institute Guo Qiang,THUIBCS,and BLBCI.
文摘Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) systems. Moreover, a newresearch paradigm has emerged as visualizationtechniques are incorporated into these models. Thisstudy divides these intersections into two researchareas: visualization for foundation model (VIS4FM)and foundation model for visualization (FM4VIS).In terms of VIS4FM, we explore the primary roleof visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FMaddresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in termsof FM4VIS, we highlight how foundation models canbe used to advance the visualization field itself. Theintersection of foundation models with visualizations ispromising but also introduces a set of challenges. Byhighlighting these challenges and promising opportunities, this study aims to provide a starting point forthe continued exploration of this research avenue.
文摘Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years for agricultural research. High spatial and temporal resolution images obtained with UAVs are ideal for many applications in agriculture. The objective of this study was to evaluate the performance of vegetation indices (VIs) derived from UAV images for quantification of plant nitrogen (N) concentration of spring wheat, a major cereal crop worldwide. This study was conducted at three locations in Idaho, United States. A quadcopter UAV equipped with a red edge multispectral sensor was used to collect images during the 2016 growing season. Flight missions were successfully carried out at Feekes 5 and Feekes 10 growth stages of spring wheat. Plant samples were collected on the same days as UAV image data acquisition and were transferred to lab for N concentration analysis. Different VIs including Normalized Difference Vegetative Index (NDVI), Red Edge Normalized Difference Vegetation Index (NDVIred edge), Enhanced Vegetation Index 2 (EVI2), Red Edge Simple Ratio (SRred edge), Green Chlorophyll Index (CIgreen), Red Edge Chlorophyll Index (CIred edge), Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) and Red Edge Triangular Vegetation Index (core only) (RTVIcore) were calculated for each flight event. At Feekes 5 growth stage, red edge and green based VIs showed higher correlation with plant N concentration compare to the red based VIs. At Feekes 10 growth stage, all calculated VIs showed high correlation with plant N concentration. Empirical relationships between VIs and plant N concentration were cross validated using test data sets for each growth stage. At Feekes 5, the plant N concentration estimated based on NDVIred edge showed one to one correlation with measured N concentration. At Feekes 10, the estimated and measured N concentration were highly correlated for all empirical models, but the model based on CIgreen was the only model that had a one to one correlation between estimated and measured plant N concentration. The observed high correlations between VIs derived from UAV and the plant N concentration suggests the significance of VIs deriving from UAVs for within-season N concentration monitoring of agricultural crops such as spring wheat.
基金Supported by the Military Scientific Research(GK20191A010240)National Key Research and Development Program of China(2018YFC1505901)。
文摘This paper generated gridded visibility(Vis)data from 1980 to 2018 over the South China Sea(SCS)based on artificial neural network(ANN),and the accuracy of the generated data was tested.Then,temporal and spatial characteristics of Vis in the area were analyzed based on the generated Vis data.The results showed that Vis in the southern SCS was generally better than that in the northern SCS.In the past 39 years,Vis in both spring and winter has improved,especially in winter at a significant increased speed of 0.37 km decade^(-1).However,Vis in both summer and autumn has decreased,especially in summer with an obvious reduction of 0.84 km decade^(-1).Overall,Vis is best in summer and worst in winter,averaging 31.89 km in summer and 20.96 km in winter,which may be related to the difference of climatic conditions and wind speed in different seasons.At the same time,probability of low Vis in spring is significantly higher than that in other seasons,especially in the northwest of Hainan Island and the northwest of Malaysia.
基金This work was supported by University of Minnesota Informatics Institute(UMII)on the Horizon Initiative and the Minnesota Long-Term Agricultural Research Network(LTARN)Program.
文摘This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn crops.Four different nitrogen treatments of 0%,80%,100%and 120%BMP(best management practice)were studied.Principal component analysis-loading(PCA-loading)was used to identify the effective wavelengths.Partial least squares(PLS)and multiple linear regression(MLR)models were built to predict different nitrogen values.Vegetation indices(VIs)were calculated and then used to build more prediction models.Both full and selected wavelengths-based models showed similar prediction trends.The overall PLS model obtained the coefficient of determination(R^(2))of 0.6535 with a root mean square error(RMSE)of 0.2681 in the prediction set.The selected wavelengths for overall MLR model obtained the R^(2) of 0.6735 and RMSE of 0.3457 in the prediction set.The results showed that the wavelengths in visible and near infrared region(350-1000 nm)performed better than the two either spectral regions(1001-1350/1425-1800 nm and 2000-2400 nm).For each data set,the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen rates.The vogelmann red edge index 2(VOG 2)performed the best among all VIs.It demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.
