The suppression of vitrinite reflectance (R0) was reported by many investigators , whereas the enhancement of vitrinite reflectance , the phenomenon that the measured R values are higher than the values that should be...The suppression of vitrinite reflectance (R0) was reported by many investigators , whereas the enhancement of vitrinite reflectance , the phenomenon that the measured R values are higher than the values that should be at the given regional rank , is in most cases unrecognized .Both R0 suppression and enhancement were observed in many basins in China , in coal seams as well as in oil source rocks , and representative examples are presented in this paper . A lot of experimental work on two series of samples was carried out for investigating the cause and mechanism of vitrinite reflectance suppression and enhancement , and effective methods for rectifying suppressed or enhanced R0 were sought on the basis of our organic geochemistry study in several basins . Our studies confirm : (1) the variations in initial hydrogen content and thermostability of vitrinite macerals are the main cause of both R0 suppression and enhancement ;(2) great care must be taken in using R0 to reflect the thermal histories of sedimentary basins because it can be not only suppressed, but also enhanced , although it has many advantages over other paleogeothermometers ; (3 ) the combined measurement of R0 and fluorescence spectrum on sporinite is an effective method for both recognizing and rectifying supressed or enhanced R0 values .展开更多
Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selec...Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.展开更多
Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the...Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.展开更多
文摘The suppression of vitrinite reflectance (R0) was reported by many investigators , whereas the enhancement of vitrinite reflectance , the phenomenon that the measured R values are higher than the values that should be at the given regional rank , is in most cases unrecognized .Both R0 suppression and enhancement were observed in many basins in China , in coal seams as well as in oil source rocks , and representative examples are presented in this paper . A lot of experimental work on two series of samples was carried out for investigating the cause and mechanism of vitrinite reflectance suppression and enhancement , and effective methods for rectifying suppressed or enhanced R0 were sought on the basis of our organic geochemistry study in several basins . Our studies confirm : (1) the variations in initial hydrogen content and thermostability of vitrinite macerals are the main cause of both R0 suppression and enhancement ;(2) great care must be taken in using R0 to reflect the thermal histories of sedimentary basins because it can be not only suppressed, but also enhanced , although it has many advantages over other paleogeothermometers ; (3 ) the combined measurement of R0 and fluorescence spectrum on sporinite is an effective method for both recognizing and rectifying supressed or enhanced R0 values .
基金funded by the National Natural Science Foundation of China(No.62076032).
文摘Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.
基金the National Grid Corporation Headquarters Science and Technology Project:Key Technology Research,Equipment Development and Engineering Demonstration of Artificial Smart Drived Electric Vehicle Smart Travel Service(No.52020118000G).
文摘Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.
