Haijiang Tai

Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network

Hyriopsis Cumingii is Chinese major fresh water pearl mussel, widely distributed in the southern provinces of China’s large and medium-sized freshwater lakes. In the management of Hyriopsis Cumingii ponds, dissolved oxygen (DO) is the key point to measure, predict and control. In this study, we analyzes the important factors for predicting dissolved oxygen of Hyriopsis Cumingii ponds, and finally chooses solar radiation(SR), water temperature(WT), wind speed(WS), PH and oxygen(DO) as six input parameters. In this paper, Elman neural networks were used to predict and forecast quantitative characteristics of water. As the dissolved oxygen in the outdoor pond is low controllability and scalability, this paper proposes a predicting model for dissolved oxygen. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The Elman NN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Experimental results show that: Elman neural network predicting model with good fitting ability, generalization ability, and high prediction accuracy, can better predict the changes of dissolved oxygen.

A Simple Temperature Compensation Method for Turbidity Sensor

Turbidity sensor for water treatment applications are based on scattered light measurement. The electrooptical characteristic of light emitter and detector has a close relationship with the environmental temperature. Fluctuations in water temperature can potentially affect electronic components and cause output signal errors in turbidity sensor. Decrease or eliminate temperature error is necessary. With this background we have taken the temperature experiment of turbidity sensor developed by us. 11 different concentrations were measured, ranging from 0 to 100NTU. Each solution was cooled to 1°C, and then gradually heated to 40°C. The output signal of sensor increased according to the rise of temperature found from the experiment. In order to compensate the temperature errors, we developed a novel method to take temperature compensation by using software. The method carried by MCU which is used to compensate the measurement errors by soft programming is simple and effective; it can improve the error compensation precision on sensors greatly.

A Review of Measurement Methods of Dissolved Oxygen in Water

Some kinds of checking methods and principle of dissolved oxygen in water were summarized. Such as: iodometric method, current determination method (Clark dissolved oxygen electrode), conductance measurement and fluorescence quenching. The advantages and disadvantages of each method were compared, and fluorescence quenching was discussed. The method uses Ruthenium complex as fluorescence sensitive reagent, which emits fluorescence under the excited light. The quenching accords with Stem-Volmer formula, and the density of oxygen could be deduced by checking fluorescence spectrum.

Design of an Intelligent PH Sensor for Aquaculture Industry

Automatic monitoring and controlling system is essential for improvement of aquaculture industry. This paper presents an intelligent PH sensor that applied to water quality monitoring. It involved signal processing, self-calibration. Using low power and high performance microcontroller MSP430F149, Smart Transducer Interface Module (STIM) based on IEEE1451standard was embedded. The design of software and hardware was described in detail, and an initial calibration experiment was carried out to establish a calibration parameter. The results indicate that the pH sensor is more accurate, reliable and easy to use, which is suitable to spread the application in aquaculture industry.

A Wireless Sink Node Using Information Fusion for Water Quality Information Collection in Factory Aquaculture

Water quality information collection is an important part of factory aquaculture. This paper proposes a kind of wireless sink nodes using information fusion for water quality information collection in factory aquaculture. In the sink nodes, Support Vector Regression and fuzzy algorithmic approach are used for information fusion. Making decisions according to information fusion, it converts the collected water quality information into a simple parameter that signified the current state of water quality. The sink nodes can eliminate redundant information, reduce information transmission, thus save energy effectively and prolong the network life.

Water Temperature Forecasting in Sea Cucumber Aquaculture Ponds by RBF Neural Network Model

Water temperature is considered to be the most important parameter which can largely determine the aquaculture production of sea cucumbers, so it is extremely important to monitor and forecast the water temperature at different water depths. As the change of water temperature is a complex process which can not be exactly described with a certain formula, the artificial neural network characterized by non-linearity, adaptivity, generalization, and model independence is a proper choice. This paper presents a RBF neural network model based on nearest neighbor clustering algorithm and puts forward four improved methods, then integrates them into an optimization model and verifies it on matlab platform. Finally, a comparison between the optimized RBF model and the original RBF model is made to confirm the excellent forecasting performance of the optimized RBF neural network model. This paper provides a relatively impeccable learning algorithm to complete the choice of radial basis clustering center in the process of RBF network design, and obtains a high forecasting precision so that the demand of water temperature forecasting in sea cucumber aquaculture ponds can be satisfied.