[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the ...[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the least square method, fre- quency distribution, aggregation index, m＊-m regression analysis and Taylor＇s pow- er law model. [Result] The field distribution of broccoli plants with clubroot disease tended to be aggregated distribution, m＇-m regression analysis showed that the el- ementary composition of the spatial distribution of diseased or infected plants was individual colony, the individuals attracted each other; the disease had obvious dis- ease focus in the field, and the individual colony showed uniform distribution pattern in the field. Taylor＇s power law showed that the spatial pattern of individual dis- eased or infected plant with clubroot disease tended to be uniform distribution with the increase of the density. On the basis of this, Iwao optimal theoretical sampling model and sequential sampling model were established, namely N =273.954 1/m- 59.698 5, To （N）=0.368 4N±1.926 8√N, respectively, it meant that when surveying N plants, if the accumulative incidence rate exceeded upper bound, the field can be set as control object; if the accumulative incidence rate didn＇t reach lower bound, it can be set as uncontrol field; if the accumulative incidence rate was between upper bound and lower bound, it should be surveyed continuously until the maximum sample size （mo=0.368 4） appeared, that was, the disease incidence was 15%, so the sampling number should be 684 plants. [Conclusion] The research results had very important instructive meaning for disease control.展开更多
Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping ...Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping time for (x n,n1). For any sequential sample (x 1,x 2,…,x τ ) and γ∈(0,1), we have given an optimal confidence limit of p with confidence level γ . Some related problems are also discussed.展开更多
The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this a...The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this area has encountered several limitations:Classifiers exhibit low training efficiency,their precision is notably reduced when dealing with imbalanced samples,and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary.This paper proposes the sequential Latin hypercube sampling(SLHS)-support vector machine(SVM)-AdaBoost algorithm,which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization.Additionally,it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm.Through comparison,the modeling efficiency,prediction accuracy on the test set,and macro-averaged values of precision,recall,and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%,5.7%,36.0%,25.0%,and 34.2%,respectively,compared with Grid-SVM.Additionally,these values are improved by 22.2%,2.1%,11.3%,2.8%,and 7.4%,respectively,compared with particle swarm optimization(PSO)-SVM-AdaBoost.Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm,the classification prediction model of anti-interference performance of UAV data links,which took factors like three-dimensional position of UAV and antenna bearing into consideration,is established and used to assess the safety of the classical flying path and optimize the flying route.It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path,whereas intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered meanwhile reducing the route length by at least 2.3%,thus benefiting both safety and operation efficiency.展开更多
Based on the data of mean population density of overwintering larvae of Dendrolimus punctatus in Shatang forest farm in Guangxi Province, the spatial pattern of overwintering larva of D. punctatus were analyzed by the...Based on the data of mean population density of overwintering larvae of Dendrolimus punctatus in Shatang forest farm in Guangxi Province, the spatial pattern of overwintering larva of D. punctatus were analyzed by the distribution index and regression model method. The results showed that the spatial pattern of overwintering larvae of D. punctatus assumed the aggregation pattern, the basic component of distribution was individual group. The optimal sampling number of forest survey and the sequential sampling analysis were presented, and the upper and low bound index for controlling D. punctatus were put forward to provide certain theoretical basis for integrated pest management.展开更多
Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible reg...Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution.展开更多
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions pl...Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions place more challenge on visual tracking. To overcome these difficulties, we formulate multiple targets tracking problem in a dynamic Markov network which consists of three coupled Markov random fields that model the following： a field for joint state of multi-target, one binary process for existence of individual target, and another binary process for occlusion of dual adjacent targets. By introducing two robust functions, we eliminate the two binary processes, and then apply a novel version of belief propagation called sequential stratified sampling belief propagation algorithm to obtain the maximum a posteriori （MAP） estimation in the dynamic Markov network, By using stratified sampler, we incorporate bottom-up information provided by a learned detector （e.g. SVM classifier） and belief information for the messages updating. Other low-level visual cues （e.g. color and shape） can be easily incorporated in our multi-target tracking model to obtain better tracking results. Experimental results suggest that our method is comparable to the state-of-the-art multiple targets tracking methods in several test cases.展开更多
Sequential samples of single precipitation event were collected by the use of specially de-signed semi-automatic sequential precipitation collector in the spring of 1988 in Guilin City. ThePH value and soluble chemica...Sequential samples of single precipitation event were collected by the use of specially de-signed semi-automatic sequential precipitation collector in the spring of 1988 in Guilin City. ThePH value and soluble chemical species such as SO, NO, NH, Ca￣(2+), Mg￣(2+), Na￣+, K`+, F￣- andCl￣- were analyzed. An apparent decrease in the concentration of all ions except H￣+ and NO wasobserved at the initial portion of the events. The relative acidity increased as the event progress.The concentration of H￣+ was the result of comprehensive actions of all ions. The average scavengingratio of events was calculated and it is found that SO was the major contributor for acid rain inGuilin and Ca￣(2+) was the important neutralizer.展开更多
For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables...For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.展开更多
基金Supported by Agricultural Key Projects of Science and Technology Program of Taizhou City in Zhejiang Province(121KY17)~~
文摘[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the least square method, fre- quency distribution, aggregation index, m＊-m regression analysis and Taylor＇s pow- er law model. [Result] The field distribution of broccoli plants with clubroot disease tended to be aggregated distribution, m＇-m regression analysis showed that the el- ementary composition of the spatial distribution of diseased or infected plants was individual colony, the individuals attracted each other; the disease had obvious dis- ease focus in the field, and the individual colony showed uniform distribution pattern in the field. Taylor＇s power law showed that the spatial pattern of individual dis- eased or infected plant with clubroot disease tended to be uniform distribution with the increase of the density. On the basis of this, Iwao optimal theoretical sampling model and sequential sampling model were established, namely N =273.954 1/m- 59.698 5, To （N）=0.368 4N±1.926 8√N, respectively, it meant that when surveying N plants, if the accumulative incidence rate exceeded upper bound, the field can be set as control object; if the accumulative incidence rate didn＇t reach lower bound, it can be set as uncontrol field; if the accumulative incidence rate was between upper bound and lower bound, it should be surveyed continuously until the maximum sample size （mo=0.368 4） appeared, that was, the disease incidence was 15%, so the sampling number should be 684 plants. [Conclusion] The research results had very important instructive meaning for disease control.
文摘Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping time for (x n,n1). For any sequential sample (x 1,x 2,…,x τ ) and γ∈(0,1), we have given an optimal confidence limit of p with confidence level γ . Some related problems are also discussed.
文摘The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this area has encountered several limitations:Classifiers exhibit low training efficiency,their precision is notably reduced when dealing with imbalanced samples,and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary.This paper proposes the sequential Latin hypercube sampling(SLHS)-support vector machine(SVM)-AdaBoost algorithm,which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization.Additionally,it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm.Through comparison,the modeling efficiency,prediction accuracy on the test set,and macro-averaged values of precision,recall,and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%,5.7%,36.0%,25.0%,and 34.2%,respectively,compared with Grid-SVM.Additionally,these values are improved by 22.2%,2.1%,11.3%,2.8%,and 7.4%,respectively,compared with particle swarm optimization(PSO)-SVM-AdaBoost.Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm,the classification prediction model of anti-interference performance of UAV data links,which took factors like three-dimensional position of UAV and antenna bearing into consideration,is established and used to assess the safety of the classical flying path and optimize the flying route.It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path,whereas intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered meanwhile reducing the route length by at least 2.3%,thus benefiting both safety and operation efficiency.
基金Supported by Fund Project in Guangxi Eco-engineering Vocational&Technical College~~
文摘Based on the data of mean population density of overwintering larvae of Dendrolimus punctatus in Shatang forest farm in Guangxi Province, the spatial pattern of overwintering larva of D. punctatus were analyzed by the distribution index and regression model method. The results showed that the spatial pattern of overwintering larvae of D. punctatus assumed the aggregation pattern, the basic component of distribution was individual group. The optimal sampling number of forest survey and the sequential sampling analysis were presented, and the upper and low bound index for controlling D. punctatus were put forward to provide certain theoretical basis for integrated pest management.
基金Foundation item. the National Natural Science Foundation of China （No. 50875164）
文摘Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.60205001,60405004 and 60021302).
文摘Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions place more challenge on visual tracking. To overcome these difficulties, we formulate multiple targets tracking problem in a dynamic Markov network which consists of three coupled Markov random fields that model the following： a field for joint state of multi-target, one binary process for existence of individual target, and another binary process for occlusion of dual adjacent targets. By introducing two robust functions, we eliminate the two binary processes, and then apply a novel version of belief propagation called sequential stratified sampling belief propagation algorithm to obtain the maximum a posteriori （MAP） estimation in the dynamic Markov network, By using stratified sampler, we incorporate bottom-up information provided by a learned detector （e.g. SVM classifier） and belief information for the messages updating. Other low-level visual cues （e.g. color and shape） can be easily incorporated in our multi-target tracking model to obtain better tracking results. Experimental results suggest that our method is comparable to the state-of-the-art multiple targets tracking methods in several test cases.
文摘Sequential samples of single precipitation event were collected by the use of specially de-signed semi-automatic sequential precipitation collector in the spring of 1988 in Guilin City. ThePH value and soluble chemical species such as SO, NO, NH, Ca￣(2+), Mg￣(2+), Na￣+, K`+, F￣- andCl￣- were analyzed. An apparent decrease in the concentration of all ions except H￣+ and NO wasobserved at the initial portion of the events. The relative acidity increased as the event progress.The concentration of H￣+ was the result of comprehensive actions of all ions. The average scavengingratio of events was calculated and it is found that SO was the major contributor for acid rain inGuilin and Ca￣(2+) was the important neutralizer.
基金supported by the National Natural Science Foundation of China under Grant No.11101396the State Key Program of National Natural Science of China under Grant No.11231010the Fundamental Research Funds for the Central Universities under Grant No.WK2040000010
文摘For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.
