1,721,068 research outputs found
Recognition of Damage Types of Blue-brick Ancient Buildings Based on Machine Learning——Taking the Macau World Cultural Heritage Buffer Zone as an Example(Training set for machine learning)
No full textAs a result of environmental and human influences, several types of surface deterioration emerge on historic buildings, resulting in a decline in the quality of these structures and even threats to their safety. In the conventional approach, assessing the surface damage on a structure involves the time-consuming and labor-intensive judgment and evaluation of trained professionals. In this study, it is suggested that the YOLOv4 machine learning model be used to automatically find five types of damage to historical brick buildings. This would make the job go more quickly. This study uses the blue brick wall buildings in the buffer zone of the global cultural heritage in Macau as an example. 1355 photographs were taken on-site of the blue brick walls, and the six most common types of damage were identified. By slicing and labeling the photos, a training set of 1000 images was created, and through 200-generation model training, the model can accurately identify and effectively identify the damage state of the blue bricks and enhance the quality judgment and evaluation of the exterior walls of historical buildings. Experiments allow us to reach the following conclusions: (1) The damage to the blue-brick ancient buildings in Macau is affected by the subtropical maritime climate. Missing paint, stains, and cracks are the main contributors to brick wall damage. (2) Ma-chine learning can help determine the type of damage to old blue-brick buildings, which is useful for managing and protecting historical buildings. (3) The model in this study can identify five types of damage: missing, cracking, plant or microbial erosion, yellowing, and pollution on the exterior walls of ancient blue-brick buildings. It is helpful to accurately identify and evaluate the damaged con-dition of the brick wall and formulate corresponding protection schemes
Predicting the distribution of COVID-19 through CGAN—Taking Macau as an example(Training set for machine learning)
No full textMachine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control
Recognition of Damage Types on the Great Wall Surface Based on Machine Learning: Taking the Shanhaiguan Great Wall as an Example(Training set for machine learning)
No full textThe Shanhaiguan Great Wall is a part of the Ming dynasty Great Wall, which is a world heritage site. Its basic structure is filled with rammed earth and gray bricks on both sides. Due to environmental influences, gray bricks on the surface will catch some damages, resulting in a decline in the quality of their structure and even threatening their safety. Traditional surface damage detection is mainly based on manual identification or manual identification after UAV aerial photography, and this will be costly in human resources. This paper uses the YOLOv4 machine learning model, taking the surface gray brick of the plain Great Wall of Shanhaiguan as an example. By slicing and labeling the photos, creating a training set, and then training the model, it automatically finds four types of damage (chalking, plant, ubiquinol, and cracking) on the surface of the Great Wall, which will solve the problem of costly human resources for manual identification after aerial photography, allowing the work to progress faster
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example(Training set for machine learning)
No full textThis dataset is about the paper "Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example".In recent years, artificial intelligence technology has widely influenced the field of design, bringing new ideas to efficiently and systematically solve urban renewal design problems. The purpose of this study is to create a stylized generation technology for building facade decoration in historic blocks, which will aid in the design and control of block style and form. The goal is to use the technical advantages of conditional generative adversarial network (CGAN) in image generation and style transfer to create a method for independently designing a specific facade decoration style by in-terpreting image data of historical block facades. The research in this paper is based on the historical district of Putian in Fujian Province, through an experiment of image data acquisition, image processing and screening, model training, image generation, and style matching of the target area. The research found that: (1) CGAN technology can better identify and generate the decorative style of historical blocks. It can realize the overall or partial scheme design of the facade; (2) in terms of adaptability, this method can provide a better scheme reference for historical block reconstruction, facade renovation, and renovation design projects. Especially for blocks with obvious decorative styles, the visualization effect is better. In addition, it also has certain reference significance for the determination and design of the facade decoration style of a specific historical building; (3) This method can better learn the internal laws of the complex block style and form so as to generate a new design with a clear decoration style attribute. It can be extended to other fields of historical heritage protection to enhance practitioners' stylized control of the heritage environment and im-prove the efficiency and ability of professional design
Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example(Training set for machine learning)
No full textThis dataset is about the paper "Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example".In recent years, artificial intelligence technology has widely influenced the field of design, bringing new ideas to efficiently and systematically solve urban renewal design problems. The purpose of this study is to create a stylized generation technology for building facade decoration in historic blocks, which will aid in the design and control of block style and form. The goal is to use the technical advantages of conditional generative adversarial network (CGAN) in image generation and style transfer to create a method for independently designing a specific facade decoration style by in-terpreting image data of historical block facades. The research in this paper is based on the historical district of Putian in Fujian Province, through an experiment of image data acquisition, image processing and screening, model training, image generation, and style matching of the target area. The research found that: (1) CGAN technology can better identify and generate the decorative style of historical blocks. It can realize the overall or partial scheme design of the facade; (2) in terms of adaptability, this method can provide a better scheme reference for historical block reconstruction, facade renovation, and renovation design projects. Especially for blocks with obvious decorative styles, the visualization effect is better. In addition, it also has certain reference significance for the determination and design of the facade decoration style of a specific historical building; (3) This method can better learn the internal laws of the complex block style and form so as to generate a new design with a clear decoration style attribute. It can be extended to other fields of historical heritage protection to enhance practitioners' stylized control of the heritage environment and im-prove the efficiency and ability of professional design
Research on the Floor Plan Design Method of Exhibition Hall in CGAN-assisted Museum Architecture(Training set for machine learning)
No full textThis paper proposes a generative adversarial network (CGAN)-based method for designing a museum exhibition hall floor plan. In the study, the basic concepts and structure of CGAN are first introduced, and then the design and training process of the CGAN model used are described in detail, and the datasets and evaluation metrics adopted are briefly described. In the Results and Analysis section, this paper presents an example of the generated floor plan design of a museum exhibition hall and evaluates and analyzes the floor plan design of a museum exhibition hall gen-erated using the method proposed in this paper. Finally, the paper summarizes the advantages and disadvantages of the proposed method and looks forward to its future development. The research results show that: (1) The method proposed in this paper takes advantage of the CGAN model and can generate a museum exhibition hall floor plan design with certain regularity according to the given conditions rather than pure random generation. (2) This method can automatically generate a variety of floor plan designs of museum exhibition halls in different styles, providing designers with more choices and flexibility. (3) This method can carry out design optimization through hu-man-computer interaction, and iterative improvement can be carried out according to user needs and feedback, which improves the practicability of the design
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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