论史书撰写艺术中的自注法——以《史记》为中心

自注法是中国史学著作中一种独特的注释方法,在现存史书中自注与正文不加区分。自注产生的时间较早,在《左传》《战国策》等先秦典籍中即已存在。司马迁在撰写《史记》的过程中大量使用自注法。《史记》中的自注类型多样,包括原有材料本身的自注、司马迁对原材料的注解以及司马迁撰写新史料的自注。《史记》中的自注有的对正文意义进行解释、阐发与补充,有的表达一种是非评价与价值判断,还有的建立一种叙述结构,并且自注是《史记》全书达到"互见"的工具。从整部《史记》来看,《太史公自序》是全书的自注。《史记》中自注与正文融合在一起达到浑然一体的艺术境界,为后世史书撰写提供了成熟的范本。

佛教与北朝史家的历史撰述

佛教对北朝史家历史撰述有很大影响。首先,佛教兴盛促使北朝一些史家意识到官修史书记载佛教的必要性。其次,佛教兴盛促使北朝一些史家撰述了一些专门的佛教史籍。最后,佛教影响了北朝一些史家历史撰述的体例和体裁。

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

Conversion factor analysis of self-balanced loading test of cast-in-situ piles based on analogue test method

Based on the characteristics of pile-soil interaction and the Mohr-Coulomb strength theory,a new method of determining the side friction at a pile-soil interaction is proposed.Combined with the actual engineering cases,the effectiveness of the analogue test method is verified by comparing it with the traditional anchor pile method and self-balanced method.Taking the self-balanced test of the bridge pile foundation in the Songhua River as an example,the conversion factor of sandy soil and weathered mudstone are confirmed by the analogue test method.The results show that the conversion factor of sandy soil and weathered mudstone in the Songhua River area should consider the geological conditions and the construction technology,etc.The standard values are relatively conservative.It is suggested that the engineering application should be properly revised.The recommended range of the conversion factor of sandy soil in this area is 0.65 to 0.85,and that of weathered mudstone is 1.0.

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and pattern recognition for decades.A two stage automatic image annotation method based on Gaussian mixture model(GMM) and random walk model(abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization(RPEM) algorithm is employed to estimate the posterior probabilities of each annotation keyword.Subsequently,a random walk process over the constructed label similarity graph is implemented to further mine the potential correlations of the candidate annotations so as to capture the refining results,which plays a crucial role in semantic based image retrieval.The contributions exhibited in this work are multifold.First,GMM is exploited to capture the initial semantic annotations,especially the RPEM algorithm is utilized to train the model that can determine the number of components in GMM automatically.Second,a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels,which is able to avoid the phenomena of polysemy and synonym efficiently during the image annotation process.Third,the random walk is implemented over the constructed label graph to further refine the candidate set of annotations generated by GMM.Conducted experiments on the standard Corel5 k demonstrate that GMM-RW is significantly more effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.

Auto-registration and Orthorecification Algorithm for the Time Series HJ-1A/B CCD Images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multi- spectral data of small constellation for environmental and disaster mitigation （HJ-1A/B） which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels （the average residuals were 37.8 m, and standard deviations were 19.8 m）. The accuracies of 45.96% validation points （VPs） were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, pereent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

Numerical simulation of nitrogen injection of goaf in fire prevention based on Finite Volume Method

The numerical simulation is used to research the influence of nitrogen injection on spontaneous combustion in goaf. The spontaneous combustion mathematical model on the coupling of air flow field, oxygen concentration field, and residual coal temperature field was established with nitrogen injection in goat＇. Then the software of numerical computation was pro- grammed by Finite Volume Method. Combined with the example, the distributions of air flow field, oxygen concentration field and residual coal temperature field at different nitrogen injection volume were obtained by the software. The results show that the nitrogen injection could effectively prevent the spontaneous combustion fire in goaf and the highest temperature in goaf decreased with the nitrogen injection volume increasing. Finally, the accuracy of the numerical simulation was verified by the temperature observation in field. The achievement of this research is of theoretical and practical significance for the prevention of coal spontaneous combustion in goaf.

Self-potential inversion based on Attention U-Net deep learning network

Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.

Image interpretation: mining the visible and syntactic correlation of annotated words

Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.