负压波法适合碳捕获、利用与封存(carbon capture utilization and storage,CCUS)技术的CO_(2)管道泄漏定位检测,但超临界CO_(2)管输周边环境及管内高压产生的噪声,影响了定位的准确性。为此,选择小波变换对含噪声的压力信号进行分解降...负压波法适合碳捕获、利用与封存(carbon capture utilization and storage,CCUS)技术的CO_(2)管道泄漏定位检测,但超临界CO_(2)管输周边环境及管内高压产生的噪声,影响了定位的准确性。为此,选择小波变换对含噪声的压力信号进行分解降噪,采用TGNET模拟软件建立CO_(2)管道泄漏模型,通过对比Botros的激波管泄放试验验证了泄漏模型的可行性。使用该泄漏模型对含噪声的压力信号进行小波降噪,再对降噪后的数据进行压差转化和互相关分析,最终得到各组压力传感器接收到压力信号的具体时间差。该泄漏模型还应用到了延长油田360000 t/a超临界CO_(2)管输方案,对人为设定的泄漏口压力噪声进行小波降噪和互相关分析。研究表明,经小波降噪后的压力信号更为稳定、精确,能够得到准确的时间差,为后续负压波法精确定位泄漏点位置提供了依据。展开更多
提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA...提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA与Fisher线性判别FLD(Fisher Linear Discriminate)结合起来,利用了以前仅用于降维的PCA特征和FLD特征相融合进行掌纹识别。基于PolyU掌纹库的实验结果表明,该方法不仅有更高的识别率,而且维数更低。展开更多
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
文摘提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA与Fisher线性判别FLD(Fisher Linear Discriminate)结合起来,利用了以前仅用于降维的PCA特征和FLD特征相融合进行掌纹识别。基于PolyU掌纹库的实验结果表明,该方法不仅有更高的识别率,而且维数更低。
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
