Abus lis overload protection . lifting is interrupted when 1 , 1 times rated load is reached . the electronic measurement method used features extremely short load detection times 安博lis過(guò)載保護(hù)裝置。該裝置當(dāng)載荷超過(guò)額定載荷的1 . 1倍時(shí)中止葫蘆起升。該裝置檢測(cè)時(shí)間短。
We construct weak classifier by a haar feature ; then weak classifiers are combined to a strong classifier in a linear way . the final classifier is built in a cascade structure , which could reject most non - face samples in the early layer . also we use integral image to quickly calculate the feature and reduce the detection time 本文以簡(jiǎn)單的haar特征結(jié)合閾值構(gòu)造弱分類器,通過(guò)adaboost學(xué)習(xí)選擇和集成弱分類器,最后按照分層結(jié)構(gòu)把集成的分類器組合在一起;同時(shí),在檢測(cè)過(guò)程中采用積分圖的方法計(jì)算特征,保證了檢測(cè)的速度。
1 . in the mpn - griess method of enumeration of nitrite - oxidizing bacteria , the nitrite concentration deeply effect the enumeration results and detection time . the o . lmmol / l nitrite concentration in the culture is the best choice for mpn - griess method and the shortest detection time is 4 week 硝酸細(xì)菌mpn ? griess計(jì)數(shù)法所用培養(yǎng)基的亞硝酸鹽濃度對(duì)計(jì)數(shù)的周期和結(jié)果均有影響,采用0 . 1mmol l亞硝酸鹽的培養(yǎng)基,進(jìn)行4周的計(jì)數(shù)培養(yǎng),是最優(yōu)的硝酸細(xì)菌mpn ? griess計(jì)數(shù)法。
In order to withstand more and more frequent compound network attacks and hacker commitment of distribution , multiobjective , multistage nowadays , improve intrusion detection efficiency under the circumstance of high band width and large - scale network , decrease false negative rate and shorten detection time , incorporating advanced machine learning techniques into ids is already a well - known thought 為了對(duì)付目前越來(lái)越頻繁出現(xiàn)的分布式、多目標(biāo)、多階段的組合式網(wǎng)絡(luò)攻擊和黑客行為,提高在高帶寬、大規(guī)模網(wǎng)絡(luò)環(huán)境下入侵檢測(cè)的效率、降低漏報(bào)率和縮短檢測(cè)時(shí)間,把先進(jìn)的機(jī)器學(xué)習(xí)方法引入到ids中來(lái)已成為一種共識(shí)。