The major phytoplankton was investigated and analyzed in landscape water of six campuses in Nanjing Xianlin University Town,and water quality was evaluated by single factor assessment method and comprehensive weighted...The major phytoplankton was investigated and analyzed in landscape water of six campuses in Nanjing Xianlin University Town,and water quality was evaluated by single factor assessment method and comprehensive weighted evaluation method.The result showed that the major phytoplankton groups were Cyanophyta,Chlorophyta and Bacillariophyta.Besides,each evaluation indicator showed that waterbodies in four campuses were eutrophicated and result of single factor evaluation showed water quality all belonged to poor category V.The result of comprehensive weighted assessment showed that waters in Nanjing Normal University and Nanjing University of Posts and Telecommunications were seriously polluted,cyanobacterial bloom appearing.Waters in Nanjing University of Chinese Medicine and Nanjing Forest Police College hadn't been eutrophicated.展开更多
Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After r...Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.展开更多
基金Supported by National Foundation for Fostering Talents in Basic Science(J0730650)~~
文摘The major phytoplankton was investigated and analyzed in landscape water of six campuses in Nanjing Xianlin University Town,and water quality was evaluated by single factor assessment method and comprehensive weighted evaluation method.The result showed that the major phytoplankton groups were Cyanophyta,Chlorophyta and Bacillariophyta.Besides,each evaluation indicator showed that waterbodies in four campuses were eutrophicated and result of single factor evaluation showed water quality all belonged to poor category V.The result of comprehensive weighted assessment showed that waters in Nanjing Normal University and Nanjing University of Posts and Telecommunications were seriously polluted,cyanobacterial bloom appearing.Waters in Nanjing University of Chinese Medicine and Nanjing Forest Police College hadn't been eutrophicated.
文摘Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.
