This paper presents a novel solution to the three-dimensional (3D) cooperative hunting ofmultiple drones that deals with surrounding a target simultaneously while navigating aroundobstacles in the cluttered dynamic 3D...This paper presents a novel solution to the three-dimensional (3D) cooperative hunting ofmultiple drones that deals with surrounding a target simultaneously while navigating aroundobstacles in the cluttered dynamic 3D environment. Meanwhile, drones avoid the air°owdownwash force created by the spinning propellers on unmanned aerial vehicles (UAVs) andtheir e®ect on the other UAVs. This solution consists of a 3D Simultaneous Encirclementstrategy, the cooperative hunting objective with a novel revised particle swarm optimization(PSO*) path planning algorithm, a °ocking theory-inspired obstacle avoidance algorithm, and acascade PI controller. Simulation results with varying conditions were carried out to validatethe e®ectiveness of the proposed solution by successfully taking care of the downwash e®ects,and having multiple hunter UAVs hunt and encircle a moving or stationary target in a dynamicor static obstacle-rich cluttered environment.展开更多
Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells.However,high sequencing cost impedes the generation of biological Hi-C data with high sequencing dept...Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells.However,high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis.Here,we developed a single-cell Hi-C simulator(scHi-CSim)that generates high-fidelity data for benchmarking.scHi-CSim merges neighboring cells to overcome the sparseness of data,samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells,and estimates the empirical distribution of restriction fragments to generate simulated data.We demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw data.Furthermore,scHi-CSim is flexible to change sequencing depth and the number of simulated replicates.We showed that increasing sequencing depth could improve the accuracy of detecting topologically associating domains.We also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.展开更多
文摘This paper presents a novel solution to the three-dimensional (3D) cooperative hunting ofmultiple drones that deals with surrounding a target simultaneously while navigating aroundobstacles in the cluttered dynamic 3D environment. Meanwhile, drones avoid the air°owdownwash force created by the spinning propellers on unmanned aerial vehicles (UAVs) andtheir e®ect on the other UAVs. This solution consists of a 3D Simultaneous Encirclementstrategy, the cooperative hunting objective with a novel revised particle swarm optimization(PSO*) path planning algorithm, a °ocking theory-inspired obstacle avoidance algorithm, and acascade PI controller. Simulation results with varying conditions were carried out to validatethe e®ectiveness of the proposed solution by successfully taking care of the downwash e®ects,and having multiple hunter UAVs hunt and encircle a moving or stationary target in a dynamicor static obstacle-rich cluttered environment.
基金supported by the National Natural Science Foundation of China(61873198 and 62132015 to L.G.,62002275 to Y.Y.,and 61621003 to S.Z.)the National Key ResearchandDevelopment ProgramoCf hina(2019YFA0709501)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA16021400 and XDPB17 to S.z.)the Key-Area Research and Development of Guangdong Province(2020B1111190001).
文摘Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells.However,high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis.Here,we developed a single-cell Hi-C simulator(scHi-CSim)that generates high-fidelity data for benchmarking.scHi-CSim merges neighboring cells to overcome the sparseness of data,samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells,and estimates the empirical distribution of restriction fragments to generate simulated data.We demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw data.Furthermore,scHi-CSim is flexible to change sequencing depth and the number of simulated replicates.We showed that increasing sequencing depth could improve the accuracy of detecting topologically associating domains.We also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.
