Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network

Abstract: Dissolved Oxygen (DO) is one of the most important parameters describing biochemical process in wastewater treatment. It is usually measured with dissolved oxygen meters, and currently galvanic and polarographic electrodes are the predominant methods. Expensive, membrane surface inactivation, and especially need of cleaning and calibrating very frequently are common disadvantages of electrode-type measuring sensors. In our work, a novel method for Prediction and Realization dissolved oxygen based-on Machine Vision and BP Neural Network was researched. Pictures of the water-body surface in aeration basins are captured and transformed into HSI space data. These data plus the correspondent measured DO values are processed with a neural network. Using the well-trained neural network, a satisfied result for classifying dissolved oxygen according to the water-body pictures has been realized.

Keywords: dissolved oxygen, machine vision, BP neural network, sewage treatment

Author: Liu Liping, Sunjin Sheng, Yin Jing-tao, Liang Na

Journal Code: jptkomputergg140110

Artikel Terkait :

Jp Teknik Komputer dd 2014

• Development of Wireless Electric Field Mill for Atmospheric Electric Field Observation
• A Novel Intrusion Detection Approach using MultiKernel Functions
• Process Improvement of LSA for Semantic Relatedness Computing
• An Algorithm Based on Wavelet Neural Network for Garment Size Selection
• Face Recognition Using Invariance with a Single Training Sample
• Dynamic DEMATEL Group Decision Approach Based on Intuitionistic Fuzzy Number
• A Reliable Web Services Selection Method for Concurrent Requests
• OWLS-CSM: A Service Profile Based Similarity Framework for Web Service Discovery
• Low Energy Adaptive Clustering Hierarchy Routing Protocol for Wireless Sensor Network
• Trusted Node-Based Algorithm to Secure Home Agent NATed IPv4 Network from IPv6 Routing Header Attacks
• Wireless Sensor Based Hybrid Architecture for Vehicular Ad hoc Networks
• Feature Selection Method Based on Improved Document Frequency
• Complex Optimization Problems Using Highly Efficient Particle Swarm Optimizer
• Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm
• Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network
• Image Deblurring Via an Adaptive Dictionary Learning Strategy
• Multi-objective Optimization Based on Improved Differential Evolution Algorithm
• Unambiguous Acquisition for Galileo E1 OS Signal Based on Delay and Multiply
• Non-Planar MOSFET Modeling with Analytical Approach
• Pre-Timed and Coordinated Traffic Controller Systems Based on AVR Microcontroller
• Cooperative Avoidance Control-based Interval Fuzzy Kohonen Networks Algorithm in Simple Swarm Robots
• Windows Communication Foundation for Banyumas Tourism and Culinary Information System
• The use of ON-OFF and ANN Controllers for Automated Irrigation System Model Based on Penman-Monteith Evapotranspiration
• Cost Forecasting Model of Transmission Project based on the PSO-BP Method
• A New Algorithm for Detecting Local Community Based on Random Walk