本文選題：車聯(lián)網(wǎng) + 群智感知��； 參考：《湖南大學學報(自然科學版)》2017年08期

【摘要】：群智感知車聯(lián)網(wǎng)利用普通用戶的手機或平板電腦等智能終端獲得交通數(shù)據(jù),解決了車聯(lián)網(wǎng)以低成本獲取足夠數(shù)據(jù)的問題,但卻凸顯了數(shù)據(jù)"質(zhì)"的問題.為此,在分析群智感知車聯(lián)網(wǎng)的數(shù)據(jù)結(jié)構(gòu)及數(shù)據(jù)異常特點的基礎(chǔ)上,提出一種適用于群智感知車聯(lián)網(wǎng)的異常數(shù)據(jù)檢測算法,并依此剔除異常數(shù)據(jù),提高數(shù)據(jù)質(zhì)量.算法利用核密度估計理論對車聯(lián)網(wǎng)數(shù)據(jù)的概率密度進行估計,進而構(gòu)建信任函數(shù)計算被檢數(shù)據(jù)的信任度,后根據(jù)統(tǒng)計學理論將信任度小于0的數(shù)據(jù)判定為異常數(shù)據(jù).最后對該算法的可行性及性能進行了仿真,結(jié)果表明該算法的性能可滿足實用需求,且對比傳統(tǒng)的統(tǒng)計檢測法在檢測率和誤檢率上具有更好的性能.
[Abstract]:Using intelligent terminals such as ordinary users' mobile phones or tablets to get traffic data, the group smart car network solves the problem of getting enough data at low cost, but it highlights the problem of data "quality". Based on the analysis of the data structure and the characteristics of data anomalies, an algorithm for detecting abnormal data is proposed, which can be used to eliminate abnormal data and improve the quality of data. The algorithm uses the kernel density estimation theory to estimate the probability density of the vehicle network data, and then constructs the trust function to calculate the trust degree of the tested data. Then, according to the statistical theory, the data whose trust degree is less than 0 are judged as abnormal data. Finally, the feasibility and performance of the algorithm are simulated, the results show that the performance of the algorithm can meet the practical needs, and it has better performance than the traditional statistical detection method in detection rate and false detection rate.
【作者單位】： 福州大學物理與信息工程學院;
【基金】：國家自然科學基金海峽聯(lián)合基金重點支持項目(U1405251);國家自然科學基金資助項目(61571129,61601126) 福建省自然科學基金資助項目(2015J01250,2016J01299)~~
【分類號】：U495

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