基于Faster R-CNN的食品图像检索和分类

面向食品领域的图像检索和分类等方面的研究成为多媒体分析和应用领域越来越受关注的研究课题之一.当前的主要研究方法基于全图提取视觉特征,但由于食品图像背景噪音的存在使得提取的视觉特征不够鲁棒,进而影响食品图像检索和分类的性能.为此,本文提出了一种基于Faster R-CNN网络的食品图像检索和分类方法.首先通过Faster R-CNN检测图像中的候选食品区域,然后通过卷积神经网络（CNN）方法提取候选区域的视觉特征,避免了噪音的干扰使得提取的视觉特征更具有判别力.此外,选取来自视觉基因库中标注好的食品图像集微调Faster R-CNN网络,以保证Faster R-CNN食品区域检测的准确度.在包括233类菜品和49 168张食品图像的Dish-233数据集上进行实验.全面的实验评估表明：基于Faster R-CNN食品区域检测的视觉特征提取方法可以有效地提高食品图像检索和分类的性能.     

基于Sentinel-3 OLCI影像的巢湖藻蓝素浓度年内动态遥感监测

藻蓝素(PC)是水体蓝藻的指示性色素,其浓度反映了蓝藻生物量,利用卫星遥感监测藻蓝素浓度年内动态对蓝藻水华的有效防控有着重要意义.根据不同季节的巢湖藻蓝素浓度实测数据与同期Sentinel-3 OLCI影像,构建机器学习回归反演模型,应用于巢湖2019年OLCI影像集上,对巢湖藻蓝素浓度的空间分布、年内变化进行遥感监测...     

Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach

ABSTRACT

Sedimentation in navigable waterways and harbours is of concern for many water and port managers. One potential source of variability in sedimentation is the annual sediment load of the river that empties in the harbour. The main objective of this study was to use some of the regularly monitored hydro-meteorological variables to compare estimates of hourly suspended sediment concentration in the Saint John River using a sediment rating curve and a model tree (M5?) with different combinations of predictors. Estimated suspended sediment concentrations were multiplied by measured flows to estimate suspended sediment loads. Best results were obtained using M5? with four predictors, returning an R² of 0.72 on calibration data and an R² of 0.46 on validation data. Total load was underestimated by 1.41% for the calibration period and overestimated by 2.38% for the validation period. Overall, the model tree approach is suggested for its relative ease of implementation and constant performance.

EDITOR M.C. Acreman; ASSOCIATE EDITOR B. Touaibia     

融合多模态特征的社会多媒体谣言检测技术研究

以微博为代表的社会媒体的蓬勃发展在加速信息交流的同时,也促使虚假谣言信息迅速在社会网络上传播,造成严重的后果.自动谣言检测问题受到了国内外学术界、产业界的广泛关注.围绕社会多媒体谣言检测这一问题,本文总结了融合多模态特征的谣言检测相关技术.首先从基本概念出发,阐述了谣言的定义和社会多媒体的特点,给出了社会多媒体谣言检测问题的定义.针对谣言检测面临的多模态特征抽取和模型构建两大难点,分别总结和归纳了各种类型的特征及其提取方法和不同的机器学习检测模型.这些特征和算法是检测谣言的基本手段,也是接下来研究的基础,可为进一步谣言检测的研究提供参考.     

Comparison of the earthquake detection abilities of PhaseNet and EQTransformer with the Yangbi and Maduo earthquakes

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide. To evaluate the generalization ability of the two models and provide insights for the development of new models, this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences. It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions, the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes. PhaseNet has a higher recall than EQTransformer, but the recall of both models is reduced by 13%–56% when compared with the results reported in the original papers. The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection performance. In designing earthquake detection models, attention should be paid to not only the balance of depth, width, and architecture but also to the quality and quantity of the training datasets. In addition, noise datasets should be incorporated during training. According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes, the Yangbi earthquake exhibited foreshock, while the Maduo earthquake showed no foreshock activity, indicating that the two earthquakes’ nucleation processes were different.     

基于BMA多模型组合的疏勒河径流预测研究

准确可靠的径流预测对于水资源的科学管理与规划具有重要意义,特别是在水资源紧缺的干旱半干旱地区,径流预测对流域内水资源高效利用与水利工程经济运行具有重要现实意义。针对径流预测通常采用单一方法进行建模与预测,难以利用各预测模型优势的问题,本文利用极限学习机（ELM）模型、支持向量机（SVM）模型、多元自适应回归样条（MARS）等机器学习方法建立了疏勒河上游未来1~7日的径流预测模型。在此基础上,运用贝叶斯模型平均（BMA）方法对ELM、SVM、MARS模型的预测结果进行组合,构建了径流组合预测模型,以获取更可靠的预测结果,并采用蒙特卡洛抽样方法获取BMA的95%置信区间,对预测结果进行了不确定性分析。结果表明：ELM、SVM、MARS模型以及BMA组合模型均适用于干旱半干旱地区的中长期日径流预测;BMA的预测精度较单一模型更高,能够提供更准确的预测值;BMA的95%置信区间对实测值覆盖率高,同时能够提供较好的确定性预测和概率预测结果。表明BMA在资料有限的条件下,表现出较单一模型更高的预测性能,可以成为干旱半干旱地区中长期日径流预测的有效方法。     

中国黄海沿岸地区环境与社会经济地域关联分析

从地域的角度出发,在分析黄海沿岸地区环境地域结构和社会经济结构的基础上,探讨了二者之间关联联系,并确定了不同地域类型改善环境质量的社会经济调控主导方向     

黄河中游地区农村经济发展战略研究

黄河中游地区是我国自然条件较差、生态环境脆弱、农村经济基础较薄弱的地区之一,本文对该地区农村经济发展的区位、资源等优势条件和生态环境、科技文化等限制因素进行了分析,对农村经济发展的现状和潜力进行了评价和分析,探讨了今后本区农村经济发展的方向与途径等问题。     

我国新世纪区域发展规划的基本发展趋向

区域发展规划是特定经济体制下引导区域经济和社会持续稳定发展的必然产物,面对市场经济条件下规划编制的“小政府,大社会”宏观背景,我国新世纪区域发展规划编制的基本趋向应该是编制计划与市场兼容型的弹性区域发展规划、多目标复合协调型的区域可持续发展规划、公平与效率兼容型社会发展规划、效益型发展规划和应用导向型发展规划。     

基于SSM的云南省产业结构水平测评

基于传统偏离-份额分析法（SSM）以及动态偏离-份额分析法,首先对中国西部地区12省（直辖市、自治区）产业结构进行横向对比,再结合云南省产业结构的历年纵向分析,旨在为云南省产业结构优化升级、经济发展方式转变提供科学参考．结果表明：1）云南省第一产业在产业结构和竞争力上均处于相对落后地位;2）云南省第二产业结构优势不明显,具备一定的竞争力优势,但同西部发达地区相比仍有一定差距;3）云南省第三产业结构优势明显,但竞争力优势不明显,产业结构优势未能很好的转化为竞争力优势;4）云南省应根据自身优势及存在问题,抓住机遇、制定对策,促进产业结构优化升级．