针对以往船舶备件需求预测精度不高,无法满足船舶综合保障的实际问题,文章建立一种基于改进红狐优化算法(improved red fox optimization,IRFO)的支持向量机回归(support vector regression,SVR)的船舶备件预测模型。为进一步提高红狐...针对以往船舶备件需求预测精度不高,无法满足船舶综合保障的实际问题,文章建立一种基于改进红狐优化算法(improved red fox optimization,IRFO)的支持向量机回归(support vector regression,SVR)的船舶备件预测模型。为进一步提高红狐优化算法(red fox optimization,RFO)的寻优精度,重构其全局搜索公式,并融合精英反向学习策略。采用基准测试函数对IRFO算法进行仿真实验,实验表明,IRFO算法比RFO算法、粒子群算法、灰狼优化算法寻优能力更强,综合性能更优。基于船舶备件历史数据,建立IRFO-SVR船舶备件预测模型,通过对比其他模型的预测结果,表明IRFO-SVR的预测效果更佳。展开更多
Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Dop...Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.展开更多
Potato(Solanum tuberosum L.) is the fourth largest food crop in the world. Low temperatures cause serious damage to potato plants every year, and freezing tolerance has become a hot spot in potato research. Galactinol...Potato(Solanum tuberosum L.) is the fourth largest food crop in the world. Low temperatures cause serious damage to potato plants every year, and freezing tolerance has become a hot spot in potato research. Galactinol synthase(GolS) is a key enzyme in the synthesis of raffinose family oligosaccharides(RFOs), and plays an important role in the response of plants to abiotic stress. In this study, the ScGolS1 gene from Solanum commersonii was cloned and introduced into the S. tuberosum cultivars 'Atlantic' and 'Desiree' via Agrobacterium-mediated transformation. Phenotyping assays showed that overexpression of ScGolS1 could significantly improve freezing tolerance in transgenic potato plants.Further physiological and biochemical experiments showed that the transgenic lines had lower relative conductivity, malondialdehyde content,and 3,3-diaminobenzidine staining and a higher plant survival rate compared with wild type(WT) under cold stress. Moreover, the C-repeat binding factors(CBF1, CBF2 and CBF3), the downstream cold-responsive genes COR413 and COR47, and the ethylene-responsive factor(ERF)transcription factor genes ERF3, ERF4 and ERF6, which function in the ethylene signaling pathway, were all induced by freezing treatment and expressed at higher levels in the ScGolS1 overexpression lines compared with WT. Besides, the expression of some genes such as MIPS, STS and RS from the RFO metabolic pathway was up-regulated under cold stress, resulting in changes in the content of some soluble sugars. This indicated that ScGolS1 overexpression altered the sugar composition and enhanced freezing tolerance in transgenic potato by inducing the ethylene and CBF signaling pathways. These results provided theoretical support and genetic resources for freezing tolerance breeding in potato.展开更多
Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason i...Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.展开更多
文摘针对以往船舶备件需求预测精度不高,无法满足船舶综合保障的实际问题,文章建立一种基于改进红狐优化算法(improved red fox optimization,IRFO)的支持向量机回归(support vector regression,SVR)的船舶备件预测模型。为进一步提高红狐优化算法(red fox optimization,RFO)的寻优精度,重构其全局搜索公式,并融合精英反向学习策略。采用基准测试函数对IRFO算法进行仿真实验,实验表明,IRFO算法比RFO算法、粒子群算法、灰狼优化算法寻优能力更强,综合性能更优。基于船舶备件历史数据,建立IRFO-SVR船舶备件预测模型,通过对比其他模型的预测结果,表明IRFO-SVR的预测效果更佳。
基金supported in part by the National Natural Foundation of China(No.62027801).
文摘Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.
基金supported by the National Natural Science Foundation of China (Grant No. 31561143006)China Agriculture Research System (Grant No. CARS-9)the Chinese Academy of Agricultural Sciences Innovation Project (Grant No. CAASASTIP-IVFCAAS)。
文摘Potato(Solanum tuberosum L.) is the fourth largest food crop in the world. Low temperatures cause serious damage to potato plants every year, and freezing tolerance has become a hot spot in potato research. Galactinol synthase(GolS) is a key enzyme in the synthesis of raffinose family oligosaccharides(RFOs), and plays an important role in the response of plants to abiotic stress. In this study, the ScGolS1 gene from Solanum commersonii was cloned and introduced into the S. tuberosum cultivars 'Atlantic' and 'Desiree' via Agrobacterium-mediated transformation. Phenotyping assays showed that overexpression of ScGolS1 could significantly improve freezing tolerance in transgenic potato plants.Further physiological and biochemical experiments showed that the transgenic lines had lower relative conductivity, malondialdehyde content,and 3,3-diaminobenzidine staining and a higher plant survival rate compared with wild type(WT) under cold stress. Moreover, the C-repeat binding factors(CBF1, CBF2 and CBF3), the downstream cold-responsive genes COR413 and COR47, and the ethylene-responsive factor(ERF)transcription factor genes ERF3, ERF4 and ERF6, which function in the ethylene signaling pathway, were all induced by freezing treatment and expressed at higher levels in the ScGolS1 overexpression lines compared with WT. Besides, the expression of some genes such as MIPS, STS and RS from the RFO metabolic pathway was up-regulated under cold stress, resulting in changes in the content of some soluble sugars. This indicated that ScGolS1 overexpression altered the sugar composition and enhanced freezing tolerance in transgenic potato by inducing the ethylene and CBF signaling pathways. These results provided theoretical support and genetic resources for freezing tolerance breeding in potato.
文摘Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.
