Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the pr...Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.展开更多
The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively stu...The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.展开更多
City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordi...City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.展开更多
足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后...足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后在Roboflow的足球数据集上进行训练,对足球比赛场景下的足球进行目标检测实现足球识别。根据实验可以得出结论:改进后的YOLOv5算法的足球识别不仅提高了足球的识别性能与精度,而且有效地提高了检测速度,具有更好的识别性能。展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a resul...Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.展开更多
The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment pro...The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.展开更多
In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and ...In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.展开更多
Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to...Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.展开更多
The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to d...The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.展开更多
Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain f...Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.展开更多
Population connectivity through seed and pollen dispersal determines the genetic diversity,adaptive potential,and demography of plant metapopulations.In wind-pollinated trees,population connectivity is typically maint...Population connectivity through seed and pollen dispersal determines the genetic diversity,adaptive potential,and demography of plant metapopulations.In wind-pollinated trees,population connectivity is typically maintained by long-distance pollen flow,counteracting the genetic differentiation generated by drift and restricted seed dispersal.Although strong population fragmentation is theoretically expected to disrupt connectivity in forest trees,empirical evidence remains scarce and inconclusive.We investigated contemporary connectivity within a network of small remnant populations of a declining conifer(Taxus baccata L.),which have been hypothesized to be largely isolated from each other.We tested this hypothesis using molecular data for adult trees and naturally recruited seedlings from all known remnants across a fragmented landscape spanning a length of 20 km,and a specifically designed statistical approach to quantify contemporary pollen and seed migration rates between populations.We additionally assessed dispersal potential using a spatially explicit parentage analysis to estimate seed and pollen dispersal kernels within one of the remnants.Estimated pairwise migration rates between populations were barely detectable for seeds,while they were larger(up to 1.1%)and significant for pollen.Both seed and pollen migration rates decreased with geographic distance between populations,more steeply in the case of pollen migration.According to parentage-based dispersal kernels,51.8% of seeds and 11.4% of pollen travel less than 25 m,whereas 0.2% of seeds and 36.1%of pollen travel more than 250 m from a source tree.In addition,1.2% of pollen can travel more than 2.5 km.We showed that strong present-day population fragmentation,with separation distances over a few kilometers between small fragments,can substantially limit the connectivity of a wind-pollinated declining tree,leading to low pollen-mediated contemporary gene flow and null or virtually null demographic connectivity via seed dispersal.展开更多
Based on the concept of open education,this study designs a specialized and creative integration course to enhance the development of teacher trainees’creative thinking,and presents a curriculum model based on the th...Based on the concept of open education,this study designs a specialized and creative integration course to enhance the development of teacher trainees’creative thinking,and presents a curriculum model based on the theory of expansive learning,non-disposable assignments,and the framework of“pre-course knowledge comprehension and assimilation,in-course thematic cooperation and discussion,and post-course thinking training.”Through the implementation of the specialized and creative integration course practice with the example of“building an online training platform for teacher qualification,”it has been verified that the course design is effective in stimulating the creative thinking of teacher trainees.In addition,the study also analyzes the developmental trajectories of teacher trainees with different levels of creativity and systematically reveals the intrinsic mechanism of the course in enhancing the creativity of teacher trainees and optimizing their thinking paths.展开更多
It is interesting that despite its long-term and widespread use in China,relatively little is known about the operational characteristics of a variable approach lane(VAL)in real world.Using one month of inductive-loop...It is interesting that despite its long-term and widespread use in China,relatively little is known about the operational characteristics of a variable approach lane(VAL)in real world.Using one month of inductive-loop detector data at ten dynamic approaches(intersection approaches with dynamic lane assignment)from different intersections in Hangzhou,China,this paper presents the results of a study materializing the flow characteristics of variable approach lanes by comparing them with adjacent normal-flow lanes under various operating conditions.The effectiveness of the results was examined in a case-control analysis by integrating 12 fixed approaches(without variable lane)as benchmark.It was found that the difference or similarity of flow rate between the variable lane and the normally-flowing lane differs under a variety of traffic volume,time-of-day,modeof-operation,and overhead lane-use guidance sign(OHS)location conditions.The study also revealed that while naturally there may be a difference in the flow rates between referencing lanes at fixed approaches,the flow difference percentage(FDP)at dynamic approaches is significantly higher.展开更多
In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and...In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.展开更多
Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for t...Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.展开更多
This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,t...This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method展开更多
基金supported by the National Natural Science Foundation of China,No.62276089。
文摘Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.
基金the financial support provided by the National Natural Science Foundation of China(NSFC)(Grant No.62173274)the National Key R&D Program of China(Grant No.2019YFA0405300)+4 种基金the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045)the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University(Grant No.PF2023046)the Open Research Subject of State Key Laboratory of Intelligent Game(Grant No.ZBKF-24-01)the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(Grant No.2024M754304)。
文摘The multi-target assignment(MTA)problem,a crucial challenge in command control,mission planning,and a fundamental research focus in military operations,has garnered significant attention over the years.Extensively studied across various domains such as land,sea,air,space,and electronics,the MTA problem has led to the emergence of numerous models and algorithms.To delve deeper into this field,this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software.The analysis includes examining keyword clustering,co-occurrence,and burst,with visual representations of the results.Following this,the paper provides an overview of current classification and modeling techniques for addressing the MTA problem,distinguishing between static multi-target assignment(SMTA)and dynamic multi-target assignment(DMTA).Subsequently,existing solution algorithms for the MTA problem are reviewed,generally falling into three categories:exact algorithms,heuristic algorithms,and machine learning algorithms.Finally,a development framework is proposed based on the"HIGH"model(high-speed,integrated,great,harmonious)to guide future research and intelligent weapon system development concerning the MTA problem.This framework emphasizes application scenarios,modeling mechanisms,solution algorithms,and system efficiency to offer a roadmap for future exploration in this area.
基金Under the auspices of the National Natural Science Foundation of China (No.72273151)。
文摘City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.
文摘足球比赛场景中球员居多、足球目标偏小且移动速度快,足球检测识别难度很大。为了解决这一问题,提出一种基于改进的YOLOv5的足球检测方法,增加使用了OTA(Optimal Transport Assignment)损失函数来优化模型提高对足球目标的识别精度,最后在Roboflow的足球数据集上进行训练,对足球比赛场景下的足球进行目标检测实现足球识别。根据实验可以得出结论:改进后的YOLOv5算法的足球识别不仅提高了足球的识别性能与精度,而且有效地提高了检测速度,具有更好的识别性能。
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
基金Fujian Province science and technology plan project under contract No.2023N0011。
文摘Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.
基金supported by the Basic Scientific Research Business Expenses of Central Universities(3072022QBZ0806)。
文摘The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.
基金National Key R&D Program of China(2022YFF1302700)Xiong’an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China(2023XAGG0065)+2 种基金Ant Group through CCF-Ant Research Fund(CCF-AFSG RF20220214)Outstanding Youth Team Project of Central Universities(QNTD202308)Beijing Forestry University National Training Program of Innovation and Entrepreneurship for Undergraduates(202310022097).
文摘In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.
文摘Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.
文摘The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.
基金supported by the National Natural Science Foundation of China(62173175,61873033)the Shandong Provincial Natural Science Foundation(ZR2024MF032)。
文摘Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.
基金supported by the National Science Centre,Poland(the grant UMO-2018/31/B/NZ8/01808 to IJC).
文摘Population connectivity through seed and pollen dispersal determines the genetic diversity,adaptive potential,and demography of plant metapopulations.In wind-pollinated trees,population connectivity is typically maintained by long-distance pollen flow,counteracting the genetic differentiation generated by drift and restricted seed dispersal.Although strong population fragmentation is theoretically expected to disrupt connectivity in forest trees,empirical evidence remains scarce and inconclusive.We investigated contemporary connectivity within a network of small remnant populations of a declining conifer(Taxus baccata L.),which have been hypothesized to be largely isolated from each other.We tested this hypothesis using molecular data for adult trees and naturally recruited seedlings from all known remnants across a fragmented landscape spanning a length of 20 km,and a specifically designed statistical approach to quantify contemporary pollen and seed migration rates between populations.We additionally assessed dispersal potential using a spatially explicit parentage analysis to estimate seed and pollen dispersal kernels within one of the remnants.Estimated pairwise migration rates between populations were barely detectable for seeds,while they were larger(up to 1.1%)and significant for pollen.Both seed and pollen migration rates decreased with geographic distance between populations,more steeply in the case of pollen migration.According to parentage-based dispersal kernels,51.8% of seeds and 11.4% of pollen travel less than 25 m,whereas 0.2% of seeds and 36.1%of pollen travel more than 250 m from a source tree.In addition,1.2% of pollen can travel more than 2.5 km.We showed that strong present-day population fragmentation,with separation distances over a few kilometers between small fragments,can substantially limit the connectivity of a wind-pollinated declining tree,leading to low pollen-mediated contemporary gene flow and null or virtually null demographic connectivity via seed dispersal.
基金2024 Gansu Province Strengthening the Awareness of the Chinese National Community Research Project“Research on the Curriculum Study Mode of AI+Introduction to the Chinese National Community”(31920250001-6)。
文摘Based on the concept of open education,this study designs a specialized and creative integration course to enhance the development of teacher trainees’creative thinking,and presents a curriculum model based on the theory of expansive learning,non-disposable assignments,and the framework of“pre-course knowledge comprehension and assimilation,in-course thematic cooperation and discussion,and post-course thinking training.”Through the implementation of the specialized and creative integration course practice with the example of“building an online training platform for teacher qualification,”it has been verified that the course design is effective in stimulating the creative thinking of teacher trainees.In addition,the study also analyzes the developmental trajectories of teacher trainees with different levels of creativity and systematically reveals the intrinsic mechanism of the course in enhancing the creativity of teacher trainees and optimizing their thinking paths.
基金supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang Province of China(No.2022C01042)the Natural Science Foundation of Zhejiang Province of China(Nos.LGF21E080002 and LR23E080002)+1 种基金the National Natural Science Foundation of China(No.72361137006)the Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies.
文摘It is interesting that despite its long-term and widespread use in China,relatively little is known about the operational characteristics of a variable approach lane(VAL)in real world.Using one month of inductive-loop detector data at ten dynamic approaches(intersection approaches with dynamic lane assignment)from different intersections in Hangzhou,China,this paper presents the results of a study materializing the flow characteristics of variable approach lanes by comparing them with adjacent normal-flow lanes under various operating conditions.The effectiveness of the results was examined in a case-control analysis by integrating 12 fixed approaches(without variable lane)as benchmark.It was found that the difference or similarity of flow rate between the variable lane and the normally-flowing lane differs under a variety of traffic volume,time-of-day,modeof-operation,and overhead lane-use guidance sign(OHS)location conditions.The study also revealed that while naturally there may be a difference in the flow rates between referencing lanes at fixed approaches,the flow difference percentage(FDP)at dynamic approaches is significantly higher.
基金The National Natural Science Foundation of China(No50308005), the National Basic Research Program of China (973Program) (No2006CB705500)
文摘In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.
文摘Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.
文摘This paper presents a new test data compression/decompression method for SoC testing,called hybrid run length codes. The method makes a full analysis of the factors which influence test parameters：compression ratio,test application time, and area overhead. To improve the compression ratio, the new method is based on variable-to-variable run length codes,and a novel algorithm is proposed to reorder the test vectors and fill the unspecified bits in the pre-processing step. With a novel on-chip decoder, low test application time and low area overhead are obtained by hybrid run length codes. Finally, an experimental comparison on ISCAS 89 benchmark circuits validates the proposed method
