Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlookin...Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments.Considering the problems of low imaging resolution,complex background environment,and large changes in target imaging of underwater sonar images,this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture,named ProNet.It progressively captures the sensitive regions in the current image where potential effective targets may exist.Guided by this basic idea,the primary technical innovation of this paper is the introduction of a foundational module structure for constructing a sonar target detection backbone network.This structure employs a multi-subspace mixed convolution module that initially maps sonar images into different subspaces and extracts local contextual features using varying convolutional receptive fields within these heterogeneous subspaces.Subsequently,a Scale-aware aggregation module effectively aggregates the heterogeneous features extracted from different subspaces.Finally,the multi-scale attention structure further enhances the relational perception of the aggregated features.We evaluated ProNet on three FLS datasets of varying scenes,and experimental results indicate that ProNet outperforms the current state-of-the-art sonar image and general target detectors.展开更多
The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring ...The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.展开更多
Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to asse...Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.展开更多
Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing r...Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.展开更多
Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missi...Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions;thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.展开更多
The bistatic Synthetic Aperture Radar (SAR) systems with separate transmitter and receiver antennas provide a new potential to imaging in the forward-looking geometry. Analysis of the Doppler property in this paper in...The bistatic Synthetic Aperture Radar (SAR) systems with separate transmitter and receiver antennas provide a new potential to imaging in the forward-looking geometry. Analysis of the Doppler property in this paper indicates the feasibility of Bistatic Forward-Looking (BFL) SAR imaging. Considering the different Doppler property determined by the two platforms in BFL SAR, a new 2-D point target spectrum is derived in our study. Based on the spectrum, an imaging method is chosen for the configuration, and the point target simulation validates the analysis.展开更多
In the spaceborne/airborne forward-looking bistatic syn- thetic aperture radar （SA-FBSAR）, due to the system platforms＇ remarkable velocity difference and the forward-looking mode, the range cell migration （RCM） ...In the spaceborne/airborne forward-looking bistatic syn- thetic aperture radar （SA-FBSAR）, due to the system platforms＇ remarkable velocity difference and the forward-looking mode, the range cell migration （RCM） not only depends on the target＇s two- dimensional location, but also varies with the range location non- linearly. And the nonlinearity is not just the slight deviation from the linear part, but exhibits evident nonlinear departure in the RCM trajectory. If the RCM is not properly corrected, nonlinear image distortions would occur. Based on the RCM model, a modified two-step RCM compensation （RCMC） method for SA-FBSAR is proposed. In this method, firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multi- plication. And then the range-dependent RCM is removed through interpolation. The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.展开更多
Bistatic forward-looking synthetic aperture radar（SAR） has many advantages and applications owing to its twodimensional imaging capability.There could be various imaging configurations because of the geometric flexi...Bistatic forward-looking synthetic aperture radar（SAR） has many advantages and applications owing to its twodimensional imaging capability.There could be various imaging configurations because of the geometric flexibility of bistatic platforms,resulting in kinds of models built independently among which there could be some similar even the same motion features.Comprehensive research on such systems in a more comprehensive and general point of view is required to address their difference and consistency.Property analysis of bistatic forwardlooking SAR with arbitrary geometry is achieved including stripmap and spotlight modes on airborne platform,missile-borne platform,and hybrid platform of both.Emphasis is placed on azimuth space variance of some key parameters significantly affecting the subsequent imaging processing,based on which the frequency spectra are further described and compared considering respective features of different platforms for frequency imaging algorithm developing.Simulation results confirm the effectiveness and correctness of our analysis.展开更多
It is a challenge to evaluate the conditions of railway track without interruption of regular traffic. In this paper, the authors introduce the detection of cavities under the railway substructure by using forward-loo...It is a challenge to evaluate the conditions of railway track without interruption of regular traffic. In this paper, the authors introduce the detection of cavities under the railway substructure by using forward-looking ground penetrating radar （FLGPR）. Main advantages of FLGPR are that such a system can illuminate a large area and can stand off a long distance over its down-looking counterpart. Two methods, frequency wave-number （F-W） synthetic aperture imaging （SAI） and beam-forming by delay and sum （DAS）, are applied to process the collected data. Analysis and measuring show that the distinct radar image of the cavity beneath the substructure 1.2 m deep can be formed by these two methods.展开更多
In the framework of an overlapping generations model, forward-looking monetary policy roles and backward-looking monetary policy rules were investigated. It is shown that the monetary steady state is more likely to be...In the framework of an overlapping generations model, forward-looking monetary policy roles and backward-looking monetary policy rules were investigated. It is shown that the monetary steady state is more likely to be indeterminate under an active forwardlooking rule than under the corresponding backward-looking rule. It is also shown that backward-looking roles can render the monetary steady state unstable.展开更多
For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the ...For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the signal-to-noise ratio(SNR)of its echo signals corresponding to different vegetations and topography also varies obviously.Owing to the reason known to all,the performance of the sparse reconstruction of compressed sensing(CS)becomes worse in the case of lower SNR,and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application.In this paper,the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the threedimensional imaging for FASAR,in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer.The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.展开更多
A Dream of Red Mansions is an ancient Chinese chapter-length fictional novel and the first of the Four Great Classical Novels of China. The judgments of the major characters within the book have been studied by many e...A Dream of Red Mansions is an ancient Chinese chapter-length fictional novel and the first of the Four Great Classical Novels of China. The judgments of the major characters within the book have been studied by many experts. The purpose of this paper is to analyse the translation strategies and their effects adopted by different translators for distinct translation purposes by comparing varied translations of the judgments and some of the allusions within them in the two English translations, Yang Xianyi and Hawkes.展开更多
基金supported in part by Youth Innovation Promotion Association,Chinese Academy of Sciences under Grant 2022022in part by South China Sea Nova project of Hainan Province under Grant NHXXRCXM202340in part by the Scientific Research Foundation Project of Hainan Acoustics Laboratory under grant ZKNZ2024001.
文摘Underwater target detection in forward-looking sonar(FLS)images is a challenging but promising endeavor.The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments.Considering the problems of low imaging resolution,complex background environment,and large changes in target imaging of underwater sonar images,this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture,named ProNet.It progressively captures the sensitive regions in the current image where potential effective targets may exist.Guided by this basic idea,the primary technical innovation of this paper is the introduction of a foundational module structure for constructing a sonar target detection backbone network.This structure employs a multi-subspace mixed convolution module that initially maps sonar images into different subspaces and extracts local contextual features using varying convolutional receptive fields within these heterogeneous subspaces.Subsequently,a Scale-aware aggregation module effectively aggregates the heterogeneous features extracted from different subspaces.Finally,the multi-scale attention structure further enhances the relational perception of the aggregated features.We evaluated ProNet on three FLS datasets of varying scenes,and experimental results indicate that ProNet outperforms the current state-of-the-art sonar image and general target detectors.
文摘The risk of gas leakage due to geological flaws in offshore carbon capture, utilization, and storage, as well as leakage from underwater oil or gas pipelines, highlights the need for underwater gas leakage monitoring technology. Remotely operated vehicles(ROVs) and autonomous underwater vehicles(AUVs) are equipped with high-resolution imaging sonar systems that have broad application potential in underwater gas and target detection tasks. However, some bubble clusters are relatively weak scatterers, so detecting and distinguishing them against the seabed reverberation in forward-looking sonar images are challenging. This study uses the dual-tree complex wavelet transform to extract the image features of multibeam forward-looking sonar. Underwater gas leakages with different flows are classified by combining deep learning theory. A pool experiment is designed to simulate gas leakage, where sonar images are obtained for further processing. Results demonstrate that this method can detect and classify underwater gas leakage streams with high classification accuracy. This performance indicates that the method can detect gas leakage from multibeam forward-looking sonar images and has the potential to predict gas leakage flow.
文摘Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.
文摘Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.
基金supported by the Key Army Pre-research Projects of China(30107030803)
文摘Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions;thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.
基金Supported by the National Natural Science Foundation of China (No. 61071165)the Aviation Science Foundation (No. 20102052024)
文摘The bistatic Synthetic Aperture Radar (SAR) systems with separate transmitter and receiver antennas provide a new potential to imaging in the forward-looking geometry. Analysis of the Doppler property in this paper indicates the feasibility of Bistatic Forward-Looking (BFL) SAR imaging. Considering the different Doppler property determined by the two platforms in BFL SAR, a new 2-D point target spectrum is derived in our study. Based on the spectrum, an imaging method is chosen for the configuration, and the point target simulation validates the analysis.
基金supported by the National Natural Science Foundation of China (61102143)the Fundamentl Research Funds for the Central Universities (ZYGX2011x003)
文摘In the spaceborne/airborne forward-looking bistatic syn- thetic aperture radar （SA-FBSAR）, due to the system platforms＇ remarkable velocity difference and the forward-looking mode, the range cell migration （RCM） not only depends on the target＇s two- dimensional location, but also varies with the range location non- linearly. And the nonlinearity is not just the slight deviation from the linear part, but exhibits evident nonlinear departure in the RCM trajectory. If the RCM is not properly corrected, nonlinear image distortions would occur. Based on the RCM model, a modified two-step RCM compensation （RCMC） method for SA-FBSAR is proposed. In this method, firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multi- plication. And then the range-dependent RCM is removed through interpolation. The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.
基金supported by the National Natural Science Foundation of China(6100121161303035+1 种基金61471283)the Fundamental Research Funds for the Central Universities(K5051202016)
文摘Bistatic forward-looking synthetic aperture radar（SAR） has many advantages and applications owing to its twodimensional imaging capability.There could be various imaging configurations because of the geometric flexibility of bistatic platforms,resulting in kinds of models built independently among which there could be some similar even the same motion features.Comprehensive research on such systems in a more comprehensive and general point of view is required to address their difference and consistency.Property analysis of bistatic forwardlooking SAR with arbitrary geometry is achieved including stripmap and spotlight modes on airborne platform,missile-borne platform,and hybrid platform of both.Emphasis is placed on azimuth space variance of some key parameters significantly affecting the subsequent imaging processing,based on which the frequency spectra are further described and compared considering respective features of different platforms for frequency imaging algorithm developing.Simulation results confirm the effectiveness and correctness of our analysis.
基金This work was supported by the National Nature Science Foundation of China under Grant No. 60472014.
文摘It is a challenge to evaluate the conditions of railway track without interruption of regular traffic. In this paper, the authors introduce the detection of cavities under the railway substructure by using forward-looking ground penetrating radar （FLGPR）. Main advantages of FLGPR are that such a system can illuminate a large area and can stand off a long distance over its down-looking counterpart. Two methods, frequency wave-number （F-W） synthetic aperture imaging （SAI） and beam-forming by delay and sum （DAS）, are applied to process the collected data. Analysis and measuring show that the distinct radar image of the cavity beneath the substructure 1.2 m deep can be formed by these two methods.
基金Project supported by National Natural Science Foundation of China （Grant No. 70071012）
文摘In the framework of an overlapping generations model, forward-looking monetary policy roles and backward-looking monetary policy rules were investigated. It is shown that the monetary steady state is more likely to be indeterminate under an active forwardlooking rule than under the corresponding backward-looking rule. It is also shown that backward-looking roles can render the monetary steady state unstable.
基金supported by the National Natural Science Foundation of China(61640006)the Natural Science Foundation of Shannxi Province,China(2019JM-386).
文摘For forward-looking array synthetic aperture radar(FASAR),the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground,and thus the signal-to-noise ratio(SNR)of its echo signals corresponding to different vegetations and topography also varies obviously.Owing to the reason known to all,the performance of the sparse reconstruction of compressed sensing(CS)becomes worse in the case of lower SNR,and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application.In this paper,the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the threedimensional imaging for FASAR,in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer.The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.
文摘A Dream of Red Mansions is an ancient Chinese chapter-length fictional novel and the first of the Four Great Classical Novels of China. The judgments of the major characters within the book have been studied by many experts. The purpose of this paper is to analyse the translation strategies and their effects adopted by different translators for distinct translation purposes by comparing varied translations of the judgments and some of the allusions within them in the two English translations, Yang Xianyi and Hawkes.
