The Korean Ritual Studies Series is a compilation of the works about Korean ritual studies, which makes a great contribution to scholarship in this area. Relying on a number of major national programs in China and South Korea, digital collation and research the Korean Ritual Studies Series was made to create into a “literature database of Korean ritual studies” by cooperation between the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU) which is a key research institute of humanities and social science approved by the Ministry of Education of the People’s Republic of China, and the research institutions of Kyungsung University including the Center for the Study of Chinese Characters in Korea, the Research Society of Korean Ritual Studies and the Academy of Korean Studies. The basic framework of the database contains six sub-databases, which are fully correlated with each other according to relevant attributes. The database provides search capability, with three search functions, including catalog index, full-text retrieval and a word index. It is a major achievement of an international cooperation program supported by a multilevel international platform. It provides a large sustainable development space for the “literature database of Korean ritual studies”, which is an indispensable and important part of the “public database of Chinese characters” created by the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU). The three key breakthroughs are needed for its further development: (I) Achieving the data association, comparative research and web publishing of Chinese and Korean ritual literature, establishing an Asian big data ritual research and application platform, supporting the research of Chinese and Korean ritual cultures, the communication history of ritual culture, the development history of Chinese characters and the research of many other disciplines, and meeting the multi-functional needs of ritual research, teaching and communication while completing a comprehensive digital connection of the internal resources of the database. (II) Deeply tapping into the massive Chinese character writing and memory data to create a 21st century database for written texts in the Sinosphere on the basis of digital processing and research of Korean ritual literature. (III) Realizing the multidimensional intelligent automatic recognition and interpretation of original literature written and memorized in Chinese characters such as ancient Chinese and Korean ritual literature, family mottoes and letters of noble families and great clans as well as folk handcopied books, and opening a new era of intelligent interpretation and research of the written texts in the Sinosphere through image recognition technology.

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and   were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.