Company Filing History:
Years Active: 2025
Title: Innovations of Changqing Su in Environmental Monitoring
Introduction
Changqing Su is a notable inventor based in Changsha, China. He has made significant contributions to the field of environmental monitoring through his innovative patents. With a total of 2 patents, his work focuses on improving the accuracy and efficiency of monitoring systems.
Latest Patents
One of his latest patents is titled "Method, device, and medium for predicting flue dust concentration." This invention discloses a method, a device, and a medium for predicting flue dust concentration. It calculates the flue dust emission amount of each batch of coal fed into a furnace based on hourly coal consumption. The invention generates a general rule between the data of coal fed into the furnace and the corresponding flue dust emission amount through training a prediction model. It accurately identifies the relationship between material and flue dust emission, reducing workloads of manual accounting and verification. This provides a reference for Continuous Emission Monitoring Systems (CEMS) flue dust monitoring data. Additionally, the use of an Adam algorithm to optimize a Back Propagation Neural Network (BPNN) allows for automatic adjustment of the learning rate for each parameter, enabling fast and efficient training of the prediction model. This invention addresses measurement errors and complex manual accounting, achieving precise measurement of flue dust emissions from coal-fired power plants.
Another significant patent is the "Layout optimization method of water quality monitoring points based on RF-C-SOM clustering algorithm." This method includes preprocessing collected water quality data to obtain preprocessed data used for training a random forest model. The model determines the feature importance of water quality indicators. Important features are selected based on feature importance and model training accuracy. Dimensionality reduction is performed on the preprocessed data to obtain dimension-reduced data. A fuzzy clustering is then conducted on this data to classify water quality sections. The initial weight values of a self-organizing mapping algorithm are determined based on this classification. The neurons are initialized, and a self-organizing mapping network model is trained with these initial weights. The result is a point clustering outcome, followed by a water quality index evaluation for the clustering result before and after screening.
Career Highlights
Changqing Su is affiliated with Hunan University of Technology and Business, where he contributes to research and development in environmental monitoring technologies. His work has garnered attention for its practical applications in improving environmental quality.
Collaborations