ISBN: (纸本)9787576309201

摘要： 线结构光3D智能相机因其成像速度快、精度高等优势在人工智能、工业检测、人脸支付、VR/AR等领域获得了广泛的应用.搭建了一种线结构光3D智能相机成像系统,开展了数据获取及处理的理论和实验研究,使用Python作为编程语言,使用Numpy、OpenCV、Open3D、Math等库,结合相机标定方法、光平面标定方法和线结构光三维重构理论,编写了三维重构算法,实现了三维重构.相对于已有的数据处理算法,该算法具有速度更快、精度更高的优势,体现了较大的应用价值.

关键词： Data fusion  

摘要： With the rapid expansion of network information and the emergence of a large number of electronic texts, how to organize and manage this massive information has become a major challenge. Automatic text categorization technology is to study how to let the machine classify unknown text through self-learning, thus solving the difficulties encountered in manual classification. Because granular computing can reduce the knowledge in solving complex problems, it is more convenient to summarize and acquire knowledge. It has become a hotspot in recent years, and it also provides new ideas for text classification research. The rough set model of granular computing can acquire knowledge by mining decision rules. The decision process is more transparent and easy to understand. It has been paid attention to and applied in text classification research. Based on the research of existing achievements, this paper makes a further study on the application of granular computing in text categorization. After analyzing the existing feature selection methods, the feature distribution is proposed based on the relationship between feature words and categories. By calculating the distribution distance between any two feature words, the feature words with similar distribution distances are aggregated, which effectively reduces the dimension of the feature space and also avoids the individual samples caused by the existing feature selection algorithm. A phenomenon that is discarded due to features. The experimental results show that the clustering method can obtain higher classification accuracy than other feature selection methods when using SVM as the classifier. SVM performs best, and the final text classification accuracy rate can reach 85.46%. According to the correlation principle of the rough set, feature selection is made for each information granularity, the selected feature is used as the condition attribute and the coordination matrix is constructed, and the most similar sample is h

关键词： Early diagnosis   Ovarian cancer   Raman spectroscopy   Machine learning   Classification models  

摘要： The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with

关键词： Artificial neural network    permeability   Well logs   Group method of data handling  

摘要： Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wastewater   Prediction model   Machine learning   K-FOLD   Process parameters  

摘要： Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD). In order to improve the prediction accuracy of the models, model optimization was conducted by introducing the hysteresis condition [Hydraulic Retention Time (HRT) of 18 h], data processing method (K-FOLD) and process parameters [dissolved oxygen (DO), sludge return ratio (SRR) and mixed liquid suspended solids (MLSS)]. Results showed that both K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) displayed excellent prediction effects, the best results of MAPE, RMSE and R2 were 7.34%/1.29/0.92(COD, KNN). The optimized models were further applied to the prediction of effluent total phosphorus (TP), total nitrogen (TN) and pH. The MAPE/RMSE/R-2 were 7.43%/0.92/0.93(TN, GBDT), 17.81%/ 0.19/0.99(TP, KNN), 0.53%/0.16/0.99 (pH, KNN) respectively, indicating high prediction accuracy. The change and comparison of modeling conditions provide a new insight to wastewater prediction models. In addition, this study is close to the actual application scenario of WWTPs operation and management, also laying a foundation for the reverse regulation of energy saving and consumption reduction of wastewater treatment plants (WWTPs).

关键词： Mixtures   Liquids   Equilibrium   Liquid liquid equilibrium   Molecules  

摘要： The intention of this article is to clarify some concepts and relevant aspects of the experimental determination and treatment of liquid-liquid equilibrium data for ternary systems. Distinction is made between solubility curve and binodal curve, setting the stability limit which is defined by the spinodal curve. In addition, it is recommended to carry out the equilibrium data correlation using semiempirical models with a thermodynamic base, over the use of empirical equations. Moreover, the misuse of these empirical equations (Hand, Bachman, Othmer-Tobias, etc.) as proof of data quality or consistency is clarified.

关键词： 数据处理   应用   社区管理   研究   中国  

ISBN: (纸本)9787547832516

摘要： “智慧社区精细化治理丛书”是对城市智慧社区的精细化、标准化、规范化建设与治理进行系统阐述的专业书籍。本书紧密围绕大数据时代环境下我国社区建设及社区治理的变化，根据我国多年来建设智慧社区的成果及经验，结合我国大数据技术应用和国外智慧社区服务系统的建设现状，分析了大数据在智慧社区建设及智慧城市规划中的价值，并从社区管理、物业管理及社区居民角度优化我国智慧社区服务信息化需求，进而提出了大数据时代背景下智慧社区的建设策略。 全书共分６章，内容包括智慧社区与大数据、智慧社区大数据的商业价值分析、智慧社区大数据的关键技术、智慧社区大数据的建设与运营、智慧社区安全与隐私保护，以及智慧社区大数据实践案例和发展趋势。

关键词： 图书情报界   《汉语主题词表》   工程技术   生命科学   自然科学版   中国科学技术信息研究所   社会科学   数据处理  

摘要： 《汉语主题词表》自1980年问世以后,经1991年进行自然科学版修订,在我国图书情报界发挥了应有作用,曾经获得国家科学技术进步二等奖。为适应网络环境下知识组织与数据处理的需要,由中国科学技术信息研究所主持,并联合全国图书情报界相关机构,自2009年开始进行重新编制工作,拟分为工程技术卷、自然科学卷、生命科学卷、社会科学卷四大部分逐步完成。目前工程技术卷和自然科学卷已出版。