例句與造句

1. The system faces to application , and tries to overcome the noise affection and shorten the recognition time
   課題的主要努力方向是針對實(shí)際應(yīng)用進(jìn)行系統(tǒng)設(shè)計(jì)、增強(qiáng)系統(tǒng)的抗噪聲性能和減少識別時(shí)間。
2. Compared with standard viterbi beam search algorithm , the adaptive algorithm that we present reduces recognition time by 35 . 56 %
   與標(biāo)準(zhǔn)viterbibeam搜索算法相比，基于活動模型數(shù)變化的自適應(yīng)viterbibeam搜索算法的搜索速度提高了35 . 56 。
3. In asset stripping accounting , the focus is the handling of a department or a branch of corporation . accounting of transferring share focuses on recognition time and valuation basis
   企業(yè)應(yīng)根據(jù)資產(chǎn)重組的具體倩況，考慮經(jīng)營風(fēng)險(xiǎn)對有關(guān)資產(chǎn)的影響，提供相關(guān)的以現(xiàn)實(shí)價(jià)值為基礎(chǔ)的會計(jì)
4. Part two , recognition of stock options . in this part , the recognition criteria , recognition period , initial recognition time of stock options are probed into
   在簡要介紹股票期權(quán)的幾種確認(rèn)觀的基礎(chǔ)上引出對股票期權(quán)的性質(zhì)探討，然后對股票期權(quán)的初始確認(rèn)標(biāo)準(zhǔn)、確認(rèn)期間及初始時(shí)點(diǎn)進(jìn)行分析。
5. On the other side , we use nearest neighbor approximation to calculate gussian mixture densities , which can reduce recognition time by 6 . 67 % compared with standard viterbi beam search algorithm
   另一方面，使用高斯混合概率密度的最近鄰快速估算方法，使標(biāo)準(zhǔn)viterbibeam搜索算法的搜索速度提高了6 . 67 。
6. It's difficult to find recognition time in a sentence. 用recognition time造句挺難的
7. The simulation results show that the algorithm not only inherits the advantages of the original algorithm , but also both volume of the navigation star database and recognition time are equivalent to 1 / 4 that of the original algorithm
   仿真結(jié)果表明本算法不但繼承了原算法的優(yōu)點(diǎn)，而且導(dǎo)航星庫的容量和識別時(shí)間都是原算法的1 / 4 。
8. When the algorithm is applied in the identification of industrial parts , comparison with the traditional bp neural network the recognition time will be shortened 2 . 8 second , and the recognition accuracy can reach more than 81 %
   將上述算法應(yīng)用于對工業(yè)零件的識別當(dāng)中，相對于傳統(tǒng)的神經(jīng)網(wǎng)絡(luò)可縮短識別時(shí)間2 . 8秒，而且識別正確率可達(dá)到81以上。
9. This algorithm effectively overcomes the contradictions of traditional algorithm among recognition success rate , recognition time and storage capacity . and this algorithm has been improved notably in aspects of database capacity and real - time , comparing with traditional star pattern recognition algorithm
   它有效地解決了傳統(tǒng)算法在識別成功率、識別時(shí)間與存儲量之間的矛盾，并且在數(shù)據(jù)庫容量、實(shí)時(shí)性等方面較傳統(tǒng)星圖識別算法有顯著改善。
10. Further more , we improve the nearest neighbor approximation method by calculat e mixtures ordered by likelihood of being the best scoring mixture . the likelihood is calculating from previously processed data . this improved method can reduce recognition time by 15 . 56 % compared with standard viterbi beam search algorithm
    本文對最近鄰快速估算方法進(jìn)行改進(jìn)，在搜索過程中根據(jù)已處理過的數(shù)據(jù)統(tǒng)計(jì)出各個(gè)高斯混合分量產(chǎn)生最高對數(shù)概率的概率，并依此預(yù)測隨后的計(jì)算中最有可能產(chǎn)生最高對數(shù)概率的高斯混合分量，優(yōu)先計(jì)算更有可能產(chǎn)生最高對數(shù)概率的高斯混合分量，使標(biāo)準(zhǔn)viterbibeam搜索算法的搜索速度提高了15 . 56 。
11. It is shown from our simulation that , when snr is no less than 15db , for awgn environment , the correct recognition rate is higher than 98 % and the average recognition time is about 3 . 2s ; for rican environment , the correct recognition rate is higher than 95 % and the average recognition time is about 3 . 26s ; for rican environment , the correct recognition rate is higher than 96 % and the recognition time is about 3 . 28s
    仿真分析結(jié)果表明：在snr = 15db條件下，在awgn環(huán)境下，該設(shè)計(jì)的正確識別率不低于98 ，自動識別的平均大約時(shí)間為3 . 2秒；在rican環(huán)境下，該設(shè)計(jì)的正確識別率不低于95 ，自動識別的平均大約時(shí)間為3 . 26秒；在rummler環(huán)境下，該設(shè)計(jì)的正確識別率不低于96 ，自動識別的平均大約時(shí)間為3 . 28秒。
12. For 10 common modulation schemes , that is , am , ssb , fm , cw , ook , psk , qpsk , fsk , msk and 16qam , the related algorithms are designed and simulated , and the corresponding successful recognition rate and the average recognition time for each algorithm are calculated
    針對am 、 cw 、 ssb 、 fm 、 psk 、 qpsk 、 msk 、 fsk 、 16qam 、 ook十種調(diào)制類型，本文分別設(shè)計(jì)了awgn信道和非awgn信道下的調(diào)制類型特征提取和自動識別算法，并進(jìn)行了計(jì)算機(jī)仿真，統(tǒng)計(jì)了正確識別率和自動識別的平均時(shí)間。