Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is deri...Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.展开更多
Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the und...Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the sottware tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.展开更多
Background China is progressing towards the goal of schistosomiasis elimination,but there are still some problems,such as difficult management of infection source and snail control.This study aimed to develop deep lea...Background China is progressing towards the goal of schistosomiasis elimination,but there are still some problems,such as difficult management of infection source and snail control.This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine,which is an intermediate source of Schistosoma japonicum infection,and to evaluate the effectiveness of the models for real-world application.Methods The dataset of livestock bovine’s spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services.The high-resolution remote sensing images were further divided into training data,test data,and validation data for model development.Two recognition models based on deep learning methods(ENVINet5 and Mask R-CNN)were developed with reference to the training datasets.The performance of the developed models was evaluated by the performance metrics of precision,recall,and F1-score.Results A total of 50 typical image areas were selected,1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model.For the ENVINet5 model,a total of 1598 records of bovine distribution were recognized.The model precision and recall were 81.9%and 80.2%,respectively.The F1 score was 0.81.For the Mask R-CNN mode,1679 records of bovine objectives were identified.The model precision and recall were 87.3%and 85.2%,respectively.The F1 score was 0.87.When applying the developed models to real-world schistosomiasis-endemic regions,there were 63 bovine objectives in the original image,53 records were extracted using the ENVINet5 model,and 57 records were extracted using the Mask R-CNN model.The successful recognition ratios were 84.1%and 90.5%for the respectively developed models.Conclusion The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples.The Mask R-CNN model has a good framework design and runs highly efficiently.The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock,which could enable precise control of schistosomiasis.展开更多
The construction of major marine infrastructure projects and the exploration and development of deep-sea mineral resources require fine imaging of seabed strata and structures.The highresolution marine seismic explora...The construction of major marine infrastructure projects and the exploration and development of deep-sea mineral resources require fine imaging of seabed strata and structures.The highresolution marine seismic exploration based on a high broadband sparker source is an important approach to reveal seabed stratum and reservoir structure,and identify geohazard.To optimize the performance of sparker seismic source,we investigated the electro-acoustic characteristics of spark discharge under conditions of different charging voltages and electrode numbers.Results show that the sound source level increased with the increase of the charging voltage,whereas the main frequency decreased when the charging voltage increases.In addition,it was found that the charging capacitance had more obvious influence on the main frequency than the sound source level did.Although the load energy decreased with increasing electrode number,the sound source level still increased but the main frequency decreased.Meanwhile,the primary to bubble(P/B)ratio increased with the increase of the electrode number.To gain a deeper insight into the electro-acoustic characteristics,we investigate the relationship between sound source level and power peak,from which a good correlation was observed.A more practical statistical analysis on the rise rate of current was processed,and a perfect logarithmic function was derived.Furthermore,we found that the main frequency was most possibly subjected to the electrical energy,especially the charging energy per electrode.The results indicate that the charging energy per electrode less than 10 J could increase the main frequency to above 300 Hz.At last,the main frequency could be reduced to 20 Hz when the charging energy of a single-electrode discharge was enhanced to over 4 kJ.This study shall be helpful in developing a sparker seismic source and improving the performance for marine engineering exploration and geohazard assessment.展开更多
HDS-SPAC,a new soil-plant-atmosphere continuum(SPAC) model,is developed for simulating water and heat transfer in SPAC.The model adopts a recently proposed hybrid dual source approach for soil evaporation and plant tr...HDS-SPAC,a new soil-plant-atmosphere continuum(SPAC) model,is developed for simulating water and heat transfer in SPAC.The model adopts a recently proposed hybrid dual source approach for soil evaporation and plant transpiration partitioning.For the above-ground part,a layer approach is used to partition available energy and calculate aerodynamic resistances,while a patch approach is used to derive sensible heat and latent heat fluxes from the two sources(soil and vegetation).For the below-ground part,soil water and heat dynamics are described by the mixed form of Richards equation,and the soil heat conductivity equation,respectively.These two parts are coupled through ground heat flux for energy transfer,root-zone water potential-dependent stomatal resistance,and surface soil water potential-dependent evaporation for water transfer.Evaporation is calculated from the water potential gradient at soil-atmosphere interface and aerodynamic resistance,and transpiration is determined using a Jarvis-type function linking soil water availability and atmospheric conditions.Some other processes,such as canopy interception and deep percolation,are also considered in the HDS-SPAC model.The hybrid dual-source approach allows HDS-SPAC to simulate heat and water transfer in an ecosystem with a large range of vegetation cover change temporally or spatially.The model was tested with observations at a wheat field in North China Plain over a time of three months covering both wet and dry conditions.The fractional crop covers change from 30% to over 90%.The results indicated that the HDS-SPAC model can estimate actual evaporation and transpiration partitioning and soil water content and temperature over the whole range of tested vegetation coverage.展开更多
基金co-supported by China Scholarship Council(201806830081)National science foundation of China(61827801,61371169,61601167,61601504)+3 种基金Jiangsu NSF(BK20161489)the open research fund of State Key Laboratory of Millimeter Waves,Southeast University(No.K201826)the Fundamental Research Funds for the Central Universities(NO.NE2017103and NT2019013)the postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX18_0293).
文摘Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.
基金This Project supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20122304120011)the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No.HEUCFR1119)
文摘Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the sottware tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.
基金National Natural Science Foundation of China(No.32161143036,No.82173633,No.81960374)Science and Technology research project of Shanghai Municipal Health Commission(No.20194Y0359)National Key Research and Development Program of China(No.2021YFC2300800,2021YFC2300803)
文摘Background China is progressing towards the goal of schistosomiasis elimination,but there are still some problems,such as difficult management of infection source and snail control.This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine,which is an intermediate source of Schistosoma japonicum infection,and to evaluate the effectiveness of the models for real-world application.Methods The dataset of livestock bovine’s spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services.The high-resolution remote sensing images were further divided into training data,test data,and validation data for model development.Two recognition models based on deep learning methods(ENVINet5 and Mask R-CNN)were developed with reference to the training datasets.The performance of the developed models was evaluated by the performance metrics of precision,recall,and F1-score.Results A total of 50 typical image areas were selected,1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model.For the ENVINet5 model,a total of 1598 records of bovine distribution were recognized.The model precision and recall were 81.9%and 80.2%,respectively.The F1 score was 0.81.For the Mask R-CNN mode,1679 records of bovine objectives were identified.The model precision and recall were 87.3%and 85.2%,respectively.The F1 score was 0.87.When applying the developed models to real-world schistosomiasis-endemic regions,there were 63 bovine objectives in the original image,53 records were extracted using the ENVINet5 model,and 57 records were extracted using the Mask R-CNN model.The successful recognition ratios were 84.1%and 90.5%for the respectively developed models.Conclusion The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples.The Mask R-CNN model has a good framework design and runs highly efficiently.The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock,which could enable precise control of schistosomiasis.
基金Supported by the National Natural Science Foundation of China(No.42276195)the Natural Science Foundation of Zhejiang Province(No.LQ22D060006)the Science Foundation of Zhejiang Sci-Tech University(No.21022092-Y)。
文摘The construction of major marine infrastructure projects and the exploration and development of deep-sea mineral resources require fine imaging of seabed strata and structures.The highresolution marine seismic exploration based on a high broadband sparker source is an important approach to reveal seabed stratum and reservoir structure,and identify geohazard.To optimize the performance of sparker seismic source,we investigated the electro-acoustic characteristics of spark discharge under conditions of different charging voltages and electrode numbers.Results show that the sound source level increased with the increase of the charging voltage,whereas the main frequency decreased when the charging voltage increases.In addition,it was found that the charging capacitance had more obvious influence on the main frequency than the sound source level did.Although the load energy decreased with increasing electrode number,the sound source level still increased but the main frequency decreased.Meanwhile,the primary to bubble(P/B)ratio increased with the increase of the electrode number.To gain a deeper insight into the electro-acoustic characteristics,we investigate the relationship between sound source level and power peak,from which a good correlation was observed.A more practical statistical analysis on the rise rate of current was processed,and a perfect logarithmic function was derived.Furthermore,we found that the main frequency was most possibly subjected to the electrical energy,especially the charging energy per electrode.The results indicate that the charging energy per electrode less than 10 J could increase the main frequency to above 300 Hz.At last,the main frequency could be reduced to 20 Hz when the charging energy of a single-electrode discharge was enhanced to over 4 kJ.This study shall be helpful in developing a sparker seismic source and improving the performance for marine engineering exploration and geohazard assessment.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50879041 and 50939004)the National Hi-Tech Research and Development Program of China (Grant No.2011BAD25B05)
文摘HDS-SPAC,a new soil-plant-atmosphere continuum(SPAC) model,is developed for simulating water and heat transfer in SPAC.The model adopts a recently proposed hybrid dual source approach for soil evaporation and plant transpiration partitioning.For the above-ground part,a layer approach is used to partition available energy and calculate aerodynamic resistances,while a patch approach is used to derive sensible heat and latent heat fluxes from the two sources(soil and vegetation).For the below-ground part,soil water and heat dynamics are described by the mixed form of Richards equation,and the soil heat conductivity equation,respectively.These two parts are coupled through ground heat flux for energy transfer,root-zone water potential-dependent stomatal resistance,and surface soil water potential-dependent evaporation for water transfer.Evaporation is calculated from the water potential gradient at soil-atmosphere interface and aerodynamic resistance,and transpiration is determined using a Jarvis-type function linking soil water availability and atmospheric conditions.Some other processes,such as canopy interception and deep percolation,are also considered in the HDS-SPAC model.The hybrid dual-source approach allows HDS-SPAC to simulate heat and water transfer in an ecosystem with a large range of vegetation cover change temporally or spatially.The model was tested with observations at a wheat field in North China Plain over a time of three months covering both wet and dry conditions.The fractional crop covers change from 30% to over 90%.The results indicated that the HDS-SPAC model can estimate actual evaporation and transpiration partitioning and soil water content and temperature over the whole range of tested vegetation coverage.