As an important pollution source, the noise pollution is always the researcher’s focus. Especially in recent years, the noise pollution is seriously harmful to the human beings’ environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.
The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.
The Opto-electronic Technology Lab of Chongqing University (OTLCU) has been working on bridge structural health
monitoring using fiber optic sensors in the past decade. A remote sensing network based on the Extrinsic Fabry-Perot
Interferometer (EFPI) fiber sensor was developed and implemented on several large bridges in Chongqing, China. In this
paper, a brief review of the OTLCU's research progress in this field was presented. Contrastive experiments between the
EFPI strain sensor and the electrical strain patch (ESP) were introduced. Both internal embedding and surface mounting
of the EFPI were studied. The design of the sensing network and two implementation examples were discussed, and
some representative monitoring results were given.
As the damage diagnosis of bridge structure is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all of the independent variables, such as the structural and environmental properties, using conventional modeling techniques. In this study, a method was introduced for damage diagnosis of bridge structure by integration of BP artificial neural network (ANN) and information fusion based on D-S evidential theory. The basic probability assignment functions for data fusion were constructed according to the demand of the damage diagnosis and the real conditions of the bridge structure. And an application example of the proposed method was demonstrated. The results showed that the integration of the two strategy can remove the shortcoming of BP ANN with remaining of its advantages and promote the identified veracity of the whole diagnosis system.
With decades of research experience on optical sensors, Optoelectronic Technology Lab of Chongqing University (OTLCU) has studied on a variety of sensors system designed for practical use in health monitoring. In OTLCU, embedded and surface mounted fiber Fabry-Perot strain sensor has been developed for monitoring the local strain of both concrete and steel truss bridge. Optoelectronic deflect meter, with a group of optical level sensor in a series connected pipe, was developed for deflection monitoring and line shape monitoring of the bridges. Laser deflect meter, with a laser pointer and a sensors array, has been also developed for a dynamic deflection monitoring of the bridges. To monitoring the 2-Dimentional displacement of the bridge, a self-calibrating imaging system was developed. All these sensor systems have been applied in different bridges successfully. This paper briefly describes principle of these optical sensing systems, and also gives some representative results of the system in practical application of bridges.
Dafosi Yangtze River Bridge, completed in August 2001, 1,176 meters long, with the longest main span of 450 meters in Asia in its design period, is one of the key projects of Yu-Qian Expressway and Out-Circle Ring and Round City Highway in Chongqing City. So it is significative to monitor the health of Dafosi Yangtze River Bridge after its completion. A remote health monitoring system is designed for Dafosi Yangtze River Bridge and implemented in the end of 2002. This system includes three subsystems, namely, sensing subsystem, local monitoring subsystem and remote monitoring subsystem. The sensing subsystem can gain the state information of the bridge via sensors, which installed in the key parts of the bridge, such as the fiber-optic strain sensors, the opto-electronic deflection sensors and the temperature sensors. Local sub-system can control all the sensors to collect health information periodically, store and transmit the health data to the remote subsystem via fiber cable. The remote subsystem can analyze and evaluate the health state of this bridge. In this paper, the whole system is introduced, and some monitoring results are presented.
In order to address application problem of fiber optic sensor in concrete, characteristics of concrete was analyzed deeply. Mechanical and metrological characteristics of both bare and packed fiber Fabry-Perot strain sensor were also analyzed in details. Modulus requirement and dimensional requirement of fiber strain sensor for concrete was deduced. A special measure of sleeve was proposed to get rid of drawback of packed fiber Fabry-Perot strain sensor in concrete. Corresponding procedures was also proposed to ensure survivability of the sensors when embedding fiber sensor into a concrete structure. An application example of fiber Fabry-Perot strain sensor network system in the Dafosi Bridge of Yangtze River at Chongqing has been presented to demonstrate the validity of this technique. With help of presented technique, 45 fiber Fabry-Perot strain sensors had been successfully embedded in 5 segments of gird during 9 months construction. The system was put into operation automatically from January 2003. Some typical results recorded by the system were presented. Constructing progress, tardo distortion trend, and temperature dependent fluctuation of gird was revealed in the result.
This paper briefly describes a health monitoring system designed for use on the Dafosi Bridge, the largest cable-stayed bridge across the Yangtze River in western China. The system can be divided into two major components, one for measurement, and one for control and data processing. The measurement system itself includes four sensing subsystems relating to: 1) fiber optic strain sensing, 2) displacement sensing, 3) temperature sensing, and 4) dynamic measurements. The control and data processing system consists of three subsystems: 1) a local computer, 2) a communication subsystem, and 3) a host computer. Sensor outputs are pre-processed locally and sent to the host computer at the management center via the Internet. The system design and implementation are reviewed, and the results of data from two sensing subsystems are presented.
This paper presents a remote state monitoring system designed for and installed on the Hongcaofang Crossroad Bridge in Chongqing, China. In this system, three kinds of sensor, one of which is new, are installed in the bridge to periodically collect strain and deflection information. To control the operation of the sensors, a local computer is integrated in the pier of the bridge. The local computer processes the data from sensors, records processed results, and sends the state information to a host computer through the local Public Service Telephone Network (PSTN) using an ordinary modem. At the other terminus, the host computer receives and analyzes the data, stores the history information, queries the health state, and extracts abnormity information regarding the bridge. With the interconnect technology available through the PSTN, real time state information can be obtained on command in the monitoring room far from the bridge. This on-line monitoring system operated on the Hongcaofang Bridge for over two years. This paper reviews some of the more important results regarding both the strain and the two-dimensional deflection of the bridge, and discusses the experience gained thus far.
The development of fiber optic sensors for safety control of civil structures is over viewed in this paper. Main principle of fiber optic sensors such as Fiber Bragg Grating sensors, fiber F- P sensors and low coherent fiber Michelson interferometer is discussed. Some typical applications of fiber optic sensors in bridges, buildings and etc. are provided. The tendency of fiber optic sensors is forecasted.