Sediment cores, suspended particles and overlying water were collected in Deep Bay, Hong Kong. Enrichment of Zn in surface sediments in the landward direction and the decreasing of exchangeable Cd, Ni and Zn in sedime...Sediment cores, suspended particles and overlying water were collected in Deep Bay, Hong Kong. Enrichment of Zn in surface sediments in the landward direction and the decreasing of exchangeable Cd, Ni and Zn in sediment from the inner bay to the outer bay indicated the influence of anthropogenic pollutants discharged from the riparian runoffs.展开更多
为提高风电出力的预测精度,提出一种基于Bayes优化的长短期记忆人工神经网络(long-short term memory,LSTM)的预测模型。首先,利用经验模态分解对风电历史出力序列进行分解,并对各分量及原始数据分别提取8个统计特征量,与预测前6个时刻...为提高风电出力的预测精度,提出一种基于Bayes优化的长短期记忆人工神经网络(long-short term memory,LSTM)的预测模型。首先,利用经验模态分解对风电历史出力序列进行分解,并对各分量及原始数据分别提取8个统计特征量,与预测前6个时刻出力值共同组成预测特征集。然后,采用绳索算法(least absolute shrinkage and selection operator,LASSO)从预测特征集中提取具有统计意义的特征子集,作为预测模型的输入。最后,提出基于Bayes超参数寻优的LSTM网络优化方法,以提高预测精度。选取湖北某市风电出力历史数据进行预测实验,结果表明:相较于BP神经网络、SVM、RBF网络、GRNN网络等预测模型,所提模型预测精度较高,特征提取方法较为合理。展开更多
Deep Web数据源的发现和其领域相关性越来越引起人们的关注和兴趣。针对在判别查询接口时,提取精度低和忽略领域相关性的问题,提出一种采用多分类器对Deep Web数据源进行自动分类和判别的方法,其思想是:对爬虫获取到的页面使用朴素贝叶...Deep Web数据源的发现和其领域相关性越来越引起人们的关注和兴趣。针对在判别查询接口时,提取精度低和忽略领域相关性的问题,提出一种采用多分类器对Deep Web数据源进行自动分类和判别的方法,其思想是:对爬虫获取到的页面使用朴素贝叶斯分类器对其进行领域相关性分类,然后使用改进的决策树分类器来对特定领域的数据源进行判定。实验结果表明此方法相比于使用单一决策树分类器有更好的性能,其召回率和精度都有所提高。展开更多
为解决传统人脸识别算法特征提取困难的问题,提出了基于卷积特征和贝叶斯分类器的人脸识别方法,利用卷积神经网络提取人脸特征,通过主成分分析法对特征降维,最后利用贝叶斯分类器进行判别分类,在ORL(olivetti research laboratory)人脸...为解决传统人脸识别算法特征提取困难的问题,提出了基于卷积特征和贝叶斯分类器的人脸识别方法,利用卷积神经网络提取人脸特征,通过主成分分析法对特征降维,最后利用贝叶斯分类器进行判别分类,在ORL(olivetti research laboratory)人脸库上进行实验,获得了99.00%的识别准确率。实验结果表明,卷积神经网络提取的人脸图像特征具有很强的辨识度,与PCA(principal component analysis)和贝叶斯分类器结合之后可有效提高人脸识别的准确率。展开更多
In the past decades, two large scale coastal engineering projects have been carried out in the Deep Bay surrounded by Shenzhen City and Hong Kong Special Administrative Region. One project is Shenzhen River channel re...In the past decades, two large scale coastal engineering projects have been carried out in the Deep Bay surrounded by Shenzhen City and Hong Kong Special Administrative Region. One project is Shenzhen River channel regulation and the other is the sea reclamation along the seashore on the Shenzhen side. The two projects are very close to the two national nature reserves, specifically Futian in Shenzhen and Mai Po in Hong Kong, which are important wetland ecosystems worldwide. This paper aims to identify and monitor the mangrove wetland changes with time series of Landsat Thematic Mapper images pre and post to the two engineering projects being launched. Coupled analysis of the image interpretation results and tidal data acquired at the same time in the context of the two works reveals that the mangrove wetland area has increased from year 1989 to 1994, and has changed little from year 1994 to 2002. Binary coding is applied to reveal the distribution image of mangrove at each phase, and the coding image shows that the construction of the two coastal engineering projects has caused frequent changes in mangrove spatial distribution. The study also shows that the change is not significant regarding to the precision of the method and the natural evolution of mangrove wetland, and the projects do not cause apparently influences upon the two national mangrove conservation zones at least for the research time period.展开更多
文摘Sediment cores, suspended particles and overlying water were collected in Deep Bay, Hong Kong. Enrichment of Zn in surface sediments in the landward direction and the decreasing of exchangeable Cd, Ni and Zn in sediment from the inner bay to the outer bay indicated the influence of anthropogenic pollutants discharged from the riparian runoffs.
文摘为提高风电出力的预测精度,提出一种基于Bayes优化的长短期记忆人工神经网络(long-short term memory,LSTM)的预测模型。首先,利用经验模态分解对风电历史出力序列进行分解,并对各分量及原始数据分别提取8个统计特征量,与预测前6个时刻出力值共同组成预测特征集。然后,采用绳索算法(least absolute shrinkage and selection operator,LASSO)从预测特征集中提取具有统计意义的特征子集,作为预测模型的输入。最后,提出基于Bayes超参数寻优的LSTM网络优化方法,以提高预测精度。选取湖北某市风电出力历史数据进行预测实验,结果表明:相较于BP神经网络、SVM、RBF网络、GRNN网络等预测模型,所提模型预测精度较高,特征提取方法较为合理。
文摘为解决传统人脸识别算法特征提取困难的问题,提出了基于卷积特征和贝叶斯分类器的人脸识别方法,利用卷积神经网络提取人脸特征,通过主成分分析法对特征降维,最后利用贝叶斯分类器进行判别分类,在ORL(olivetti research laboratory)人脸库上进行实验,获得了99.00%的识别准确率。实验结果表明,卷积神经网络提取的人脸图像特征具有很强的辨识度,与PCA(principal component analysis)和贝叶斯分类器结合之后可有效提高人脸识别的准确率。
文摘In the past decades, two large scale coastal engineering projects have been carried out in the Deep Bay surrounded by Shenzhen City and Hong Kong Special Administrative Region. One project is Shenzhen River channel regulation and the other is the sea reclamation along the seashore on the Shenzhen side. The two projects are very close to the two national nature reserves, specifically Futian in Shenzhen and Mai Po in Hong Kong, which are important wetland ecosystems worldwide. This paper aims to identify and monitor the mangrove wetland changes with time series of Landsat Thematic Mapper images pre and post to the two engineering projects being launched. Coupled analysis of the image interpretation results and tidal data acquired at the same time in the context of the two works reveals that the mangrove wetland area has increased from year 1989 to 1994, and has changed little from year 1994 to 2002. Binary coding is applied to reveal the distribution image of mangrove at each phase, and the coding image shows that the construction of the two coastal engineering projects has caused frequent changes in mangrove spatial distribution. The study also shows that the change is not significant regarding to the precision of the method and the natural evolution of mangrove wetland, and the projects do not cause apparently influences upon the two national mangrove conservation zones at least for the research time period.
