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走航式电阻率法探测平原水库库底砂体效果分析及应用

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中国海洋大学学报（自然科学版） 2024年第9期54卷 110-121页

作者：付垚张宇丰张振超孙计同杨彬济南市勘察测绘研究院山东济南250000 中国海洋大学环境科学与工程学院山东青岛266100 山东省海洋环境地质工程重点实验室山东青岛266100

平原水库库底沉积层中的砂体会对水库运行以及穿过库底地下空间工程建设的安全造成不可忽视的影响,一种快速且准确识别库底砂体空间位置的探测手段可为破解以上问题提供技术支持。本文以位于黄河流域的济南鹊山水库为研究区,基于数值模... 详细信息

平原水库库底沉积层中的砂体会对水库运行以及穿过库底地下空间工程建设的安全造成不可忽视的影响,一种快速且准确识别库底砂体空间位置的探测手段可为破解以上问题提供技术支持。本文以位于黄河流域的济南鹊山水库为研究区,基于数值模拟与原位探测结果,分析评价了走航式电阻率法识别平原水库库底砂体的最优装置参数和能力,并将所得结论在原位探测中应用、验证。研究结果表明,选用通用走航探测航速(航速2.5 m/s)对平原水库(水深6 m)进行探测时,走航式电阻率法对埋深浅(埋深6 m)的砂体有较好的理论分辨能力。对于薄层深埋砂体(厚度10 m)则难以识别;砂体探测顶、底界位置与实际顶、底界位置间的偏移距离与砂体顶界埋深、砂体长度呈正相关关系。砂体厚度增大会提高不同顶界埋深条件下砂体界面偏移距离差异,偏移距离正相关于深埋砂体厚度,负相关于浅埋砂体厚度;建立了修正原位探测结果并识别砂体的方法。基于原位探测结果所识别的砂体形态并不精确,其厚度普遍厚于实际砂体厚度约0~3 m。厚度小于3 m的薄砂体则难以被该方法识别。

关键词：走航式电阻率法数值模拟平原水库水下砂体探测效果

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2019年天津近岸海域海洋垃圾分布特征研究

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天津科技 2022年第11期49卷 12-14页

作者：韩龙韩彤夏妍梦付振超巩元帅天津市生态环境监测中心天津300191 天津天滨瑞成环境技术工程有限公司天津300191

2019年秋季,对天津近岸海域海洋垃圾的分布特征进行了调查研究。结果表明:海面漂浮的大块和特大块垃圾平均密度为7.7个/km^(2),主要材质为塑料类(84.6%);表层海水中中小块垃圾的平均分布密度为626.8个/km^(2),主要材质为塑料类(63.6%);... 详细信息

2019年秋季,对天津近岸海域海洋垃圾的分布特征进行了调查研究。结果表明:海面漂浮的大块和特大块垃圾平均密度为7.7个/km^(2),主要材质为塑料类(84.6%);表层海水中中小块垃圾的平均分布密度为626.8个/km^(2),主要材质为塑料类(63.6%);海滩垃圾平均分布密度为423.6个/km^(2),主要材质为塑料类(52.5%)。通过样品类型判断,天津近岸海域海洋垃圾可能主要来源于陆上。

关键词：海洋垃圾塑料类分部特征天津近岸海域

Antibacterial Activity of Novel 18β-Glycyrrhetinic Hydrazide or Amide Derivatives

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Chemical Research in Chinese Universities 2021年第3期37卷 662-667页

作者： ZHANG Ling fu Yihong DING Yue MENG Jiao WANG zhenchao WANG Peiyi State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationCenter for R&n of Fine ChemicalsGuizhou UniversityGuiyang550025P.R.China College of Pharmacy Drug Synthetic Engineering Laboratory of Guizhou ProvinceGuizhou UniversityGuiyang550025P.R.China

Through a facile structural modification on the natural bioactive ingredient 18β-glycyrrhetinic acid(GA),a series of novel GA hydrazide or amide derivatives was obtained,and their final molecular frameworks were characterized by NMR and HRMS *** bioassays revealed that some of the GA hydrazide or amide derivatives were able to suppress the growth of three tested plant ***,compound 3c exhibited excellent in vitro activity against Xanthomonas oryzae ***(Xoo),Pseudomonas syringae ***(Psa),and Xanthomonas axonopodis ***(Xac),providing the EC_(50) values of 5.89,16.1,and 3.64µg/mL,*** data were better than those of the positive controls thiodiazole copper(92.7,77.8,and 89.9µg/mL,respectively)and bismerthiazol(31.1,125.6,and 77.4µg/mL,respectively).In addition,in vivo experiments suggested that,compared with thiodiazole copper(41.93%and 39.73%,respectively),compound 3c exerted prominently curative and protective activities against rice bacterial leaf blight at 200µg/mL with the control effects of 52.36%and 51.40%,*** these obtained results,GA hydrazide or amide derivatives could serve as the feasible leads for exploring highly bioactive substrates.

关键词： 18β-Glycyrrhetinic Hydrazide or amide derivative Synthesis Antibacterial activity

An improved binocular localization method for apple based on fruit detection using deep learning

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农业信息处理(英文) 2023年第2期10卷 276-287页

作者： Tengfei Li Wentai Fang Guanao Zhao Fangfang Gao zhenchao Wu Rui Li Longsheng fu Jaspreet Dhupia College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China Department of Mechanical Engineering The University of AucklandPrivate Bag 92019AucklandNew Zealand

Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest *** detection and localization of target fruit is necessary for robotic apple *** accuracy has a great influence on localization *** current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex *** the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly ***,a deep learningbased method was developed to accurately detect and locate the position of *** different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment ***,this paper proposed an improved binocular localization method for apple based on fruit detection using deep ***,apples of binocular images were detected by Faster *** that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected ***,template matching with parallel polar line constraint was used to match apples in left and right ***,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization *** this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an ***,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate *** showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,*** indicated that the p

关键词： Deep learning Object detection Faster R-CNN Template matching Image segmentation Binocular localization

Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning

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Information Processing in Agriculture 2023年第1期10卷 1-10页

作者： Ruizhe Yang zhenchao Wu Wentai Fang Hongliang Zhang Wenqi Wang Longsheng fu Yaqoob Majeed Rui Li Yongjie Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Xi’an Agriculture Machinery Management and Extension Station Xi’anShaanxi 710065China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China Faculty of Agricultural Engineering and Technology University of AgricultureFaisalabad 38000Pakistan Center for Precision and Automated Agricultural Systems Washington State UniversityProsserWA 99350USA Suide County Lanhuahua Ecological Food Co. Ltd.SuideShaanxi 718000China

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic *** and rotten leaves are main types of abnormal leaves in hydroponic *** study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,*** Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce *** binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models *** showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training *** took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,*** was more robust than MLR in detecting yellow and rotten leaves of ***,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

关键词： Hydroponic lettuce Machine learning Image processing Abnormal leaves Automatic sorting

Description of the FDML Laser with Quasi-steady State Model of the SOA

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Optics and Photonics Journal 2013年第2期3卷 61-65页

作者： Zhi Wang Limei Zhang Lanlan Liu zhenchao Sun Yingfeng Liu fu Wang Institute of Optical Information School of Science Beijing Jiaotong University Key Laboratory of Luminescence and Optical Information Ministry of Education Beijing China

Experiments and simulations demonstrate that an SOA-based ring cavity can operate as a tunable laser, wavelength- swept laser or Fourier-domain-mode-locking laser according to the relation between the roundtrip freque... 详细信息

关键词： Mode Locked Laser Semiconductor Optical Amplifiers Tunable Filter

利用太赫兹光非对称解复用器对归零伪随机码占空比压缩的全光速率倍增

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中国激光 2016年第4期43卷 124-129页

作者：孙振超王智(指导) 吴重庆王甫李强北京交通大学理学院光信息科学与技术研究所发光与光信息技术教育部重点实验室北京100044

利用太赫兹非对称解复用器(TOAD)对伪随机比特序列(PRBS)归零(RZ)码占空比压缩后经多路延迟叠加来实现速率倍增。实验上将周期为2~7-1、速率为2.5 Gb/s的伪随机RZ码占空比由50%压缩至12.5%后经4路精确延时叠加,保持码型不变,速率提升4倍... 详细信息

利用太赫兹非对称解复用器(TOAD)对伪随机比特序列(PRBS)归零(RZ)码占空比压缩后经多路延迟叠加来实现速率倍增。实验上将周期为2~7-1、速率为2.5 Gb/s的伪随机RZ码占空比由50%压缩至12.5%后经4路精确延时叠加,保持码型不变,速率提升4倍至10 Gb/s,相对于原伪随机码,保证误码率10^(-9)的功率代价为2 dB。

关键词：光通信信号处理占空比压缩速率倍增太赫兹非对称解复用器半导体光放大器

黄土高原典型塬区冬小麦田地表辐射平衡各分量特征分析

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太阳能学报 2009年第1期30卷 12-18页

作者：李振朝韦志刚文军符睿刘远永刘蓉中国科学院寒区旱区环境与工程研究所西部气候环境与灾害实验室兰州730000 中国科学院研究生院北京100039 云南省气象科学研究所昆明650034

利用2006年4～7月份实际观测资料,分析了黄土高原典型塬区冬小麦生长过程中不同天气条件下的地表辐射平衡各分量特征,并对比分析了冬小麦下垫面与裸土下垫面下的辐射平衡特征,发现:在不同天气条件下辐射平衡日变化特征有很大变化,冬小... 详细信息

利用2006年4～7月份实际观测资料,分析了黄土高原典型塬区冬小麦生长过程中不同天气条件下的地表辐射平衡各分量特征,并对比分析了冬小麦下垫面与裸土下垫面下的辐射平衡特征,发现:在不同天气条件下辐射平衡日变化特征有很大变化,冬小麦下垫面地面向上长波辐射晴天、阴天、降水天时依次减小,到达峰值时间约滞后总辐射峰值到达时间1h。大气向下长波辐射与地表向上长波辐射恰恰相反,晴天时量值最小,基本稳定在300W·m^(-2),阴天和降水天时依次增大。小麦田与裸地相比,地表向上短波辐射比裸地小;地表向上长波辐射白天裸地大于小麦地,夜间相反;净辐射白天小麦地略大于裸地,夜间则裸地大于小麦地。在辐射平衡中地表长波贡献最大,大气长波辐射、总辐射和净辐射依次减小,反射辐射最小。植被覆盖度高时,土壤植被系统吸收的总辐射和净辐射也高。

关键词：黄土高原典型塬区冬小麦辐射收支

利用开关窗口连续可调的太赫兹非对称解复用器实现归零码占空比调节

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中国激光 2014年第12期41卷 230-236页

作者：孙振超王智吴重庆王甫林青简阳天北京交通大学理学院光信息科学与技术研究所发光与光信息技术教育部重点实验室北京100044

提出利用开关窗口连续可调的太赫兹非对称解复用器(TOAD)实现对归零(RZ)码占空比的调节,分析了半导体光放大器(SOA)载流子恢复时间对伴随窗口的影响。实验上对2.5Gb/s的RZ码占空比进行调节,实现了由50%(200ps)压缩到25%(100ps)和10%(40p... 详细信息

提出利用开关窗口连续可调的太赫兹非对称解复用器(TOAD)实现对归零(RZ)码占空比的调节,分析了半导体光放大器(SOA)载流子恢复时间对伴随窗口的影响。实验上对2.5Gb/s的RZ码占空比进行调节,实现了由50%(200ps)压缩到25%(100ps)和10%(40ps),以及由25%展宽到50%。进一步研究结果表示,该结构对占空比展宽有上限,但对占空比的压缩上限远远不止实验中的40ps,如果继续减小开关窗口宽度,利用该结构可以获得更小占空比RZ脉冲信号,实现皮秒甚至亚皮秒窄脉冲。

关键词：光通信光信号处理占空比调节太赫兹非对称解复用器伴随窗口半导体光放大器