Xin Huang – författare

This book constitutes the proceedings of the 20th International Conference on Web Information Systems Engineering, WISE 2019, held in Hong Kong, China, in November 2019. Due to the problems/protests in Hong Kong, WISE 2019 was postponed from November 26-30, 2019 until January 19-22, 2020. 

Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs.

In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.

This book is the fifth volume of the proceedings of the 4th GeoShanghai International Conference that was held on May 27 - 30, 2018. This volume, entitled “Tunneling and Underground Construction”, covers the recent advances and technologies in tunneling and underground structure engineering. It presents the state-of-the-art planning philosophy, theories, experiments, methodologies and findings in the related areas. The importance of underground space utilization to the development of human society is also addressed. The challenges and future directions of underground engineering are highlighted.  The topics include but are not limited to the tunneling and underground construction induced ground deformation, mechanical behaviors of segmental lining systems, tunneling in challenging situations, maintenance tactic and emergency counter-measures. The book may benefit researchers and scientists from the academic fields of tunneling and underground structure engineering as well as practical engineers from the industry.

Each of the papers included in this book received at least two positive peer reviews. The editors would like to express their sincerest appreciation to all of the anonymous reviewers all over the world, for their diligent work.

This book constitutes the refereed proceedings, presented on the 20th International Conference on Web Information Systems Engineering, WISE 2019 and on The International Workshop on Web Information Systems in the Era of AI, held in Hong Kong and Macau, China. Due to the problems in Hong Kong, WISE 2019 has been postponed until January 2020.

The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition.

Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices.

This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.

This book is composed of eight chapters, introducing the authors’ research and application achievements in the hazard-causing system and disaster evaluation of water and mud inrush in tunnels over the past 10 years. Through a large number of case studies and analysis, and on the basis of existing research, this book puts forward 3 categories and 11 types of tunnel water and mud inrush hazard-causing systems and 4 typical water and mud inrush disaster-forming modes. The authors carefully study the typical cases of tunnel water and mud inrush hazard-causing system, discuss the types of karst water system, structural characteristics, macro-geological identification, engineering identification, karst tunnel route selection principles and evaluation methods in detail, and then develop a dynamic evaluation method of tunnel water and mud inrush construction risk interval and an evaluation method for the resistance body. Ultimately, the authors put forward a systematic identification methodof tunnel water and mud inrush disaster, which integrates geological identification, geophysical exploration identification, and drilling identification, and construct a dynamic management and analysis platform for tunnel water and mud inrush cases.

This book is used as a reference book for teachers, graduate students, and undergraduates in colleges and universities of civil engineering, transportation, water conservancy and hydropower, mining, geology, etc., and also as a reference for technicians in related engineering fields.

The intelligent diagnosis and maintenance of the machine mainly includes condition monitoring, fault diagnosis, performance degradation assessment and remaining useful life prediction, which plays an important role in protecting people''s lives and property. In actual engineering scenarios, machine users always hope to use an automatic method to shorten the maintenance cycle and improve the accuracy of fault diagnosis and prognosis. In the past decade, Artificial Intelligence applications have flourished in many different fields, which also provide powerful tools for intelligent diagnosis and maintenance.

This book highlights the latest advances and trends in new generation artificial intelligence-driven techniques, including knowledge-driven deep learning, transfer learning, adversarial learning, complex network, graph neural network and multi-source information fusion, for diagnosis and maintenance of rotating machinery. Its primary focus is on the utilization of advanced artificial intelligence techniques to monitor, diagnose, and perform predictive maintenance of critical structures and machines, such as aero-engine, gas turbines, wind turbines, and machine tools.

The main markets of this book include academic and industrial fields, such as academic institutions, libraries of university, industrial research center. This book is essential reading for faculty members of university, graduate students, and industry professionals in the fields of diagnosis and maintenance.