The current GIS can only deal with 2-D or 2.5-D information on the earth surface. A new 3-D data structure and data model need to be designed for the 3-D GIS. This paper analyzes diverse 3-D spatial phenomena from min...The current GIS can only deal with 2-D or 2.5-D information on the earth surface. A new 3-D data structure and data model need to be designed for the 3-D GIS. This paper analyzes diverse 3-D spatial phenomena from mine to geology and their complicated relations, and proposes several new kinds of spatial objects including cross-section, column body and digital surface model to represent some special spatial phenomena like tunnels and irregular surfaces of an ore body. An integrated data structure including vector, raster and object-oriented data models is used to represent various 3-D spatial objects and their relations. The integrated data structure and object-oriented data model can be used as bases to design and realize a 3-D geographic information system.展开更多
The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When thi...The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.展开更多
To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior ...To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.展开更多
Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking p...Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.展开更多
Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the...Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.展开更多
目的提升健康食品包装技术前瞻研判的精度和深度,厘清该领域创新演进路径。方法融合论文、专利与社交媒体多数据源,以双向编码器表示的主题建模(Bidirectional encoder representations from transformers for topic modeling,BERTopic...目的提升健康食品包装技术前瞻研判的精度和深度,厘清该领域创新演进路径。方法融合论文、专利与社交媒体多数据源,以双向编码器表示的主题建模(Bidirectional encoder representations from transformers for topic modeling,BERTopic)挖掘技术主题,通过设计“影响力、增长性、连贯性、创新性、不确定性/模糊性”5项指标体系筛选前沿主题,再结合社交媒体情感计算检验社会接受度,最后利用支持向量回归(Support vector regression,SVR)预测新兴技术未来发展趋势。结果生物基可降解材料、植物源抗菌涂层、RFID营养追踪、多光谱新鲜度传感和微胶囊营养靶向递送等5项技术被识别为新兴技术方向,表现出高成长性与市场渗透潜力。结论研究基于多源数据融合,提出将市场需求纳入技术识别研究,并预测新兴技术发展,为健康食品包装领域研发布局与战略决策提供了新的研究视角。展开更多
基金Project supported by the National Natural Science Foundation of China (No.49871066)
文摘The current GIS can only deal with 2-D or 2.5-D information on the earth surface. A new 3-D data structure and data model need to be designed for the 3-D GIS. This paper analyzes diverse 3-D spatial phenomena from mine to geology and their complicated relations, and proposes several new kinds of spatial objects including cross-section, column body and digital surface model to represent some special spatial phenomena like tunnels and irregular surfaces of an ore body. An integrated data structure including vector, raster and object-oriented data models is used to represent various 3-D spatial objects and their relations. The integrated data structure and object-oriented data model can be used as bases to design and realize a 3-D geographic information system.
文摘The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.
基金Sponsored by the Beijing Municipal Natural Science Foundation(4082027)
文摘To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.
基金Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064)Fundamental Research Funds for Central Universities of China(N130108001)+1 种基金National High Technology Research and Development Program of China(2015AA043802)General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186)
文摘Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.
基金National Natural Science Foundation of China(No.61374140)the Youth Foundation of National Natural Science Foundation of China(No.61403072)
文摘Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.
文摘目的提升健康食品包装技术前瞻研判的精度和深度,厘清该领域创新演进路径。方法融合论文、专利与社交媒体多数据源,以双向编码器表示的主题建模(Bidirectional encoder representations from transformers for topic modeling,BERTopic)挖掘技术主题,通过设计“影响力、增长性、连贯性、创新性、不确定性/模糊性”5项指标体系筛选前沿主题,再结合社交媒体情感计算检验社会接受度,最后利用支持向量回归(Support vector regression,SVR)预测新兴技术未来发展趋势。结果生物基可降解材料、植物源抗菌涂层、RFID营养追踪、多光谱新鲜度传感和微胶囊营养靶向递送等5项技术被识别为新兴技术方向,表现出高成长性与市场渗透潜力。结论研究基于多源数据融合,提出将市场需求纳入技术识别研究,并预测新兴技术发展,为健康食品包装领域研发布局与战略决策提供了新的研究视角。
