基于AD5933阻抗测量芯片的人体经穴阻抗动态监测系统研制

目的：设计人体经穴阻抗动态经络监测系统，明确人体经穴阻抗在低中频范围内响应规律。方法：系统采用STM32系列单片机STM32F103RBT6作为控制器，对阻抗测量芯片AD5933内部寄存器读写从而控制阻抗测量，外接ADG711模拟开关以实现不同量程范围，选用SGM4782双路模拟开关选通A、B两个通道，并在AD5933芯片的Vout脚和Vin脚间加入基于AD844的恒流源，采样数据通过SD卡读入上位机并分析。结果：系统能够实时反映经穴阻抗在1～128 kHz扫频激励下阻抗曲线及数值。结论：基于AD5933阻抗测量芯片的人体经穴阻抗动态经络监测系统可以准确反映经穴阻抗信息，可作为进一步明确针灸经穴阻抗特性研究工具。

A Neural Network Method for Monitoring Snowstorm: A Case Study in Southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.

Prediction of bridge temperature field and its effect on behavior of bridge deflection based on ANN method

In recent years, the bridge safety monitoring has been paid more attention in engineering field. How- ever, the financial and material resources as well as human resources were costly for the traditional monitoring means. Besides, the traditional means of monitoring were low in accuracy. From an engineering example, based on neural network method and historical data of the bridge monitoring to construct the BP neural network model with dual hidden layer strueture, the bridge temperature field and its effect on the behavior of bridge deflection are forecasted. The fact indicates that the predicted biggest error is 3.06% of the bridge temperature field and the bridge deflection behavior under temperature field affected is 2. 17% by the method of the BP neural net-work, which fully meet the precision requirements of the construction with practical value.

Study on gas monitoring technology based on information fusion

In view of the deficiency of current gas monitoring systems in coal mine roadwayexcavation, a two-level information fusion technology, which adopted the adaptiveweighted algorithm and the BP neural network technology, was applied to gas monitoring.The results show that the adaptive weighted algorithm can realize self-regulation by decreasingthe weight value of the failed sensor automatically, so as to eliminate the effect ofthe failed sensor and ensure the effectiveness and accuracy of the gas monitoring system.The BP neural network can not only effectively predict the gas gush quantity of the excavationroadway, but also accurately calculate the gas concentration in the region whereone or more sensors have failed, so as to provide the basis for judging the safety status ofthe roadway excavation.The experiments prove the superiority and feasibility of the applicationof information fusion in gas monitoring.

Neural Networks for Condition Monitoring of Wind Turbines Gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

Application of Neural Networks for Monitoring Mechanical Defects of Rotating Machines

Good monitoring of the deterioration in rotating machinery can result in reduced maintenance costs by minimizing the loss of production due to the number of machine breakdown and decreasing in the number of spare parts. In the present paper, a prognostic method based on recurrent neural networks is applied to forecast the rate of machine deterioration. Promising results have been obtained through the application of this method to the prediction of vibration based fault trends of an auxiliary gearbox of a power generation plant. This method evaluates also the seriousness of damage caused by faults.

Seismic Health Monitoring of Foundations Using Artificial Neural Networks

Damage identification plays an important role in structural health monitoring systems. Despite variety in damage identification methods, little attention has been paid to the seismic damage identification of foundations. When shear walls serve as the lateral load resistance system of structures, foundations may subject to the high level of concentrated moment and shear forces. Consequently, they can experience severe damage. Since such damage is often internal and not visible, visual inspections cannot identify the location and the severity of damage. Therefore, a robust method is required for damage localization and quantification of foundations. According to the concept of performance-based seismic design of structures, the seismic behavior of foundations is considered as Force-Controlled. Therefore, for damage identification of foundation, internal forces should be estimated during ground motions. In this study, for real-time seismic damage detection of foundations, a method based on artificial neural networks was proposed. A feed-forward multilayer neural network with one hidden layer was selected to map input samples to output parameters. The lateral displacements of stories were considered as the input parameters of the neural network while moment and shear force demands at critical points of foundations were taken into account as the output parameters. In order to prepare well-distributed data sets for training the neural network, several nonlinear time history analyses were carried out. The proposed method was tested on the foundation of a five-story concrete shear wall building. The obtained results revealed that the proposed method was successfully estimated moment and shear force demands at the critical points of the foundation.

Thansparent and Explicable Boiler Fouling Monitoring with Fuzzy Neural Newtwork

Fouling on boiler heating surfaces is one of the important factors that damage boiler’s economical per-formance and safety With on-line monitoring of foiling states on boiler heating surfaces, it is possible to optimize sootblower system, to visualize fouling states, to improve performance, as well as to remedy the insufficlellcy of experiment research in boiler heating surface fouling process. New method based on Fuzzy Neural Network (FNN) is presented to monitor fouling states on boiler heating surfaces on-line.Compared with regular methods, since FNN’s reasoning process is transpareot and comprehensible,it is pooible to explain and comprehend reasoning processt which makes the FNN based system perform as an additional operation consulting system.