Abstract. In this paper, a new approach to terrain generation based on terrain examples is proposed. Existing procedural algorithms for generation of terrain have several shortcomings. The most popular approach, fractal-based terrain generation, is efficient, but is difficult for users to control. In this paper, we provide a semiautomatic method of terrain generation that uses a four-process genetic algorithm approach to produce a variety of terrain types using only intuitive user inputs. We allow users to specify a rough sketch of terrain silhouette map, retrieve terrain examples based on support vector machine (SVM) from the terrain dataset, cut a region from the terrain examples and fill in the terrain silhouette map. We also generate a photorealistic texture based on the aerial or satellite images. Consequently, we generate the terrain which has both geometrical data and texture data and provide a balance between user input and real-world data capture unmatched.
Local fiction is an important part of Chinese literature. As the carrier of local fiction, fork language has gradually attracted the attention of translation scholars and has become a new subject derived from domestic translation academia. This paper explores the translation style and translation strategies of "fork language" between sinologists Howard Goldblatt and Julia Lovell, so as to promote the further "going out" of Chinese culture and provide a reference for telling Chinese stories and disseminating Chinese. 1. INTRODUCTION In recent years, more and more domestic scholars have begun to pay close attention to the translation of Chinese fork language. In July 2015, Professor Zhou Lingshun's "Practical Criticism Research on Chinese 'Local Language' Translation into English" was approved as a key project of the National Social Science Fund, which has become another milestone in academic research in this field. Since the approval of the project, Professor Zhou has set up three research columns in Shandong Foreign Language Teaching [17], Contemporary Foreign Language Research [17] and Journal of Beijing Second Foreign Language Institute [18], and published several academic papers to study the translation strategies of fork language. The results are remarkable. Professor Wang Baorong's new book, Foreign Experience: A Study of the English Translation and Dissemination of Shaoxing Regional Culture in Lu Xun's Novels, reviews the translation strategies of Shaoxing's regional culture in seven English versions through reviewing the translation and introduction of Lu Xun's novels in the English world. It is known as a masterpiece in the field of foreign and communication studies of "local language" in China. [19], "A masterpiece on the translation and introduction of Chinese regional culture to foreign countries" [11]. Today, China's most popular sinologists include the late Professor William Lyells, as well as Howard Goldblatt and Julia Lovell. Among them, Howard Goldblatt is the most famous one, whose translation representatives not only include Chinese well-known works by Mo Yan, such as Red Sorghum, Rich Breast and Wide Hips, but also the works by Xiao Hong, Liu Zhenyun and Jia Pingwa. Howard Goldblatt has translated more than 60 works of 30 Chinese writers and is the most prolific translator in Chinese novels in history. His translation is rigorous and exquisite. Gladys B.Tayler commented that he "made Chinese literature take on the color of contemporary British and American literature"[12].On the basis of loyalty to the original text, Howard Goldblatt believes that translation is a kind of cross-cultural communication and communication[5].Therefore, most of Howard Goldblatt's translations were adopted assimilation as the main strategy and foreignization as the supplement. Julia Lovell is a Cenozoic-era Sinologist and Translator in England. Along with the American translator Howard Goldblatt, she is also known as the "Gemini Constellation" in the translation of contemporary Chinese literature in Britain and Ameri...
In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. A genetic algorithm is introduced to iterate the feature sets of angles and line segments, and the optimal features are finally derived in the process of cross evolution of generations to improve the recognition accuracy. And the character library is used as input items for average grouping for experiments, and the obtained feature sets are put into the position matrix and compared with the samples in the database one by one. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm.
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