王琿 葛益嫻 劉清惓 韓上邦 孫啟云
關(guān)鍵詞: 雨滴直徑; 輸出電壓; 導(dǎo)電圓環(huán); BP神經(jīng)網(wǎng)絡(luò); 梯度下降法; Sigmoid函數(shù)
中圖分類(lèi)號(hào): TN02?34; P412.13 ? ? ? ? ? ? ? ? ? 文獻(xiàn)標(biāo)識(shí)碼: A ? ? ? ? ? ? ? ? ? ? 文章編號(hào): 1004?373X(2019)03?0180?04
Abstract: In order to reduce the size, weight, and cost of the traditional raindrop spectrometer, and improve the measurement accuracy of the raindrop spectrum, a raindrop spectrometer composed of new raindrop spectrum probe, Cortex?M3 ARM processor, and high?precision and low?noise measurement circuit is proposed, which is based on isometric conductive ring structure. The output voltage difference caused by different raindrop diameter is used in the raindrop spectrometer for measurement. The Sigmoid function is taken as the transfer function, and the gradient descent method is used to establish the BP neural network model of output voltage and raindrop diameter of the raindrop spectrometer. The algorithm based on this model is embedded into the ARM processor to obtain the raindrop diameter. The raindrop spectrometer can realize the observation of raindrop whose diameter within 2.67~5.56 mm, and the measurement error is less than ± 0.1 mm.
Keywords: raindrop diameter; output voltage; conductive ring; BP neural network; gradient descent method; Sigmoid function
雨滴譜反映了雨滴數(shù)量隨直徑的分布情況,是降雨觀測(cè)的重要組成部分。文獻(xiàn)[1]在研究層狀云和對(duì)流云降水雨滴譜特征時(shí)發(fā)現(xiàn),層狀云降水的譜寬小于對(duì)流云降水,且對(duì)于這兩種降水而言,降雨強(qiáng)度越大,降水中的大粒子越多。文獻(xiàn)[2]在評(píng)估人工降雨系統(tǒng)效能時(shí)發(fā)現(xiàn),通過(guò)提高人工降雨的雨滴直徑和自然狀態(tài)的相似性,可以提高人工模擬降雨的質(zhì)量。此外,雨滴中值粒徑和坡面產(chǎn)沙量具有較高的相關(guān)性,雨滴中值粒徑越大,土壤侵蝕越嚴(yán)重[3]。因此,雨滴譜的觀測(cè)具有重要意義。
目前,應(yīng)用較為廣泛的雨滴譜儀有沖擊型雨滴譜儀Joss?Waldvogel(JWD)和Parsivel激光雨滴譜儀。JWD沖擊型雨滴譜儀由傳感器、處理器和電纜組成,根據(jù)雨水滴落在傳感器表面產(chǎn)生的電脈沖的大小測(cè)量雨滴直徑。其觀測(cè)結(jié)果受雨滴的大小、速度、形狀以及背景噪聲的影響,會(huì)低估暴雨中小雨滴的數(shù)量[4]。Parsivel激光雨滴譜儀主要由發(fā)射機(jī)、接收機(jī)、控制、運(yùn)算和存儲(chǔ)電路組成,根據(jù)雨滴通過(guò)采樣區(qū)域時(shí)的光信號(hào)的變化測(cè)量雨滴直徑。激光雨滴譜儀使用簡(jiǎn)單,維護(hù)方便,但是體積大、成本高,存在重疊誤差[5]。為了減小雨滴譜觀測(cè)儀器的體積、重量和成本,提高測(cè)量精度,本文設(shè)計(jì)一種基于BP神經(jīng)網(wǎng)絡(luò)算法的雨滴譜儀,利用等距導(dǎo)電圓環(huán)結(jié)構(gòu)的雨滴譜探頭、Cortex?M3 ARM處理器及高精度低噪聲測(cè)量電路實(shí)現(xiàn)對(duì)雨滴的觀測(cè)。
針對(duì)目前市面上雨滴譜儀存在體積大、成本高等問(wèn)題,結(jié)合BP神經(jīng)網(wǎng)絡(luò)算法,本文設(shè)計(jì)了一種基于等距導(dǎo)電圓環(huán)結(jié)構(gòu)的新型雨滴譜探頭、Cortex?M3 ARM處理器及高精度低噪聲測(cè)量電路的雨滴譜儀。實(shí)驗(yàn)結(jié)果表明,該雨滴譜儀實(shí)現(xiàn)了對(duì)直徑為2.67~5.56 mm雨滴的觀測(cè),測(cè)量誤差小于±0.1 mm。但該雨滴譜儀仍然存在一些問(wèn)題,如探頭表面積水也會(huì)影響雨滴觀測(cè)。因此,比較和分析熱蒸發(fā)、超聲波霧化和機(jī)械振動(dòng)初始等方法,選擇合適的方式對(duì)探頭除水是本文未來(lái)的研究重點(diǎn)。
參考文獻(xiàn)
[1] SUBRATA K D, MAHEN K, KAUSTAV C, et al. Raindrop size distribution of different cloud types over the Western Ghats using simultaneous measurements from micro?rain radar and disdrometer [J]. Atmospheric research, 2017, 86: 72?82.
[2] 郭東靜,陳錫云,馬晶,等.基于雨滴譜的人工降雨系統(tǒng)效能評(píng)估[J].水土保持學(xué)報(bào),2015,29(4):85?90.
GUO Dongjing, CHEN Xiyun, MA Jing, et al. Reliability ana?lysis for artificial rainfall system based on raindrop spectrum detector [J]. Journal of soil and water conservation, 2015, 29(4): 85?90.
[3] 盛世博,王瑄,盛思遠(yuǎn),等.沈陽(yáng)地區(qū)天然降雨雨滴特征對(duì)坡面產(chǎn)沙量的影響[J].水土保持研究,2017,24(2):12?16.
SHENG Shibo, WANG Xuan, SHENG Siyuan, et al. Influence of natural rainfall on slope sediment yield in Shenyang region [J]. Research of soil and water conservation, 2017, 24(2): 12?16.
[4] 朱亞喬,劉元波.地面雨滴譜觀測(cè)技術(shù)及特征研究進(jìn)展[J].地球科學(xué)進(jìn)展,2013,28(6):685?693.
ZHU Yaqiao, LIU Yuanbo. Advances in measurement techniques and statistics features of surface raindrop size distribution [J]. Advances in earth science, 2013, 28(6): 685?693.
[5] 胡子浩,濮江平,張歡,等.Parsivel激光雨滴譜儀觀測(cè)較強(qiáng)降水的可行性分析和建議[J].氣象科學(xué),2014,34(1):25?31.
HU Zihao, PU Jiangping, ZHANG Huan, et al. Feasibility analyses and recommendations of parsiveloptical distrometer in measurements of strong precipitation [J]. Journal of the meteorological sciences, 2014, 34(1): 25?31.
[6] ALSHAWABKEH A N, SHEAHAN T C, WU X. Coupling of electrochemical and mechanical processes in soils under DC fields [J]. Mechanics of materials, 2004, 36(56): 453?465.
[7] 焦李成,楊淑媛,劉芳,等.神經(jīng)網(wǎng)絡(luò)七十年:回顧與展望[J].計(jì)算機(jī)學(xué)報(bào),2016,39(8):1697?1716.
JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Seventy years beyond neural networks: retrospect and prospect [J]. Chinese journal of computers, 2016, 39(8): 1697?1716.
[8] 羅云芳.一種BP神經(jīng)網(wǎng)絡(luò)校正算法的實(shí)驗(yàn)室智能溫控系統(tǒng)研究[J].現(xiàn)代電子技術(shù),2015,38(20):84?91.
LUO Yunfang. Research on laboratory′s intelligent temperature control system based on BP network correction algorithm [J]. Modern electronics technique, 2015, 38(20): 84?91.
[9] 王蒙,常勝,王豪.一種自適應(yīng)訓(xùn)練的BP神經(jīng)網(wǎng)絡(luò)FPGA設(shè)計(jì)[J].現(xiàn)代電子技術(shù),2016,39(15):115?118.
WANG Meng, CHANG Sheng, WANG Hao. FPGA based design of BP neural network with adaptive training [J]. Modern electronics technique, 2016, 39(15): 115?118.
[10] 舒小健,高太長(zhǎng),劉西川,等.基于降水為物理特征測(cè)量?jī)x的雨滴形狀觀測(cè)與分析[J].氣象,2017,43(1):91?100.
SHU Xiaojian, GAO Taichang, LIU Xichuan, et al. Observation and analysis of raindrop shape based on the precipitation micro?physical characteristics sensor [J]. Meteorological, 2017, 43(1): 91?100.