800 publications from this institution
This paper proposes a structural damage identification approach based on the power spectral density transmissibility (PSDT), which is developed to formulate the relationship between two sets of auto-spectral density functions of output responses. The accuracy of response reconstruction with PSDT is investigated and the damage identification in structures is conducted with measured acceleration responses from the damaged state. Numerical studies on a seven-storey plane frame structure are conducted to investigate the performance of the proposed damage identification approach. The initial finite element model of the structure and measured acceleration measurements from the damaged structure are used for the identification with a dynamic response sensitivity-based model updating method. The simulated damages can be identified accurately without and with a 5% noise effect included in the simulated responses. Experimental studies on a steel plane frame structure in the laboratory are performed to further verify the accuracy of response reconstruction with PSDT and validate the proposed damage identification approach. The locations of the introduced damage are detected accurately and the stiffness reductions in the damaged elements are identified close to the true values. The identification results demonstrated the accuracy of response reconstruction as well as the correctness and efficiency of the proposed damage identification approach.
In order to quickly and accurately implement emergency rescue after earthquake and save lives and property, this paper proposes an emergency rescue training system based on immersion technology. The system uses virtual reality, augmented reality and mixed reality technology to construct a real and virtual interwoven training environment. Compared with the traditional emergency rescue training, this method is not limited by time and site, and enables the trainees to immerse themselves in the earthquake environment for operation. The system consists of four main functional modules: cognition of fault movement, recognition of earthquake precursor, simulation of earthquake scene, and training of emergency rescue.
Micro-fabricated cantilevers have been reported recently as miniaturized, rapid response, ultrasensitive sensors elements suitable for various chemical and bio-sensing applications. However, the alignment of the cantilever with the optical read-out system can be challenging and typically involves a bulky free-space optical detection system. We propose using cantilevers aligned to the core of an optical fibre during the fabrication process to address this issue. Focussed Ion Beam (FIB) machining has been demonstrated as capable of fabricating fibre-top cantilevers. Here we demonstrate techniques to design and fabricate micro-cantilevers using a combination of laser machining and FIB processing to fabricate sensing cantilevers onto the end of standard and multi-core fibres (MCF). In this way the cantilever can be aligned with the core of the fibre therefore offering stable and accurate means of optically addressing the cantilever. Use of MCF offers the potential for a single probe capable of making multiple measurements in a confined measurement volume, to determine multiple species of interest, or to provide background reference measurements for example. The optical cavity formed between the fibre and the cantilever is monitored using low-cost optical sources and fibre coupled spectrometers to demonstrate a practical measurement system. This can readily achieve <50nm resolution using analysis based upon recovering the free spectral range using the Fast Fourier Transform to calculate the final cavity length.
The cardinality constrained mean–variance (CCMV) portfolio selection model aims to identify a subset of the candidate assets such that the constructed portfolio has a guaranteed expected return and minimum variance. By formulating this model as the mixed-integer quadratic program (MIQP), the exact solution can be solved by a branch-and-bound algorithm. However, computational efficiency is the central issue in the time-sensitive portfolio investment due to its NP-hardness properties. To accelerate the solution speeds to CCMV portfolio optimization problems, we develop various heuristic methods based on techniques such as continuous relaxation, l1-norm approximation, integer optimization, and relaxation of semi-definite programming (SDP). We evaluate our heuristic methods by applying them to the US equity market dataset. The experimental results show that our SDP-based method is effective in terms of the computation time and the approximation ratio. Our SDP-based method performs even better than a commercial MIQP solver when the computational time is limited. In addition, several investment companies in China have adopted our methods, gaining good returns. This paper sheds light on the computation optimization for financial investments.
In this paper, mode distribution in large-mode-area (LMA) 25/400 fiber was investigated while attempts to recognize and sort different modes with their combination were carried out on a CNN net via Tensorflow. VGG16 model was chosen as the backbone net through several test to increase precision. The model was trained on a dataset including 6000 pictures in 15 categories. And the final accuracy was up to 0.98. It indicates that recognizing modes in high power fiber laser system based on a CNN net was a feasible plan in the mode control assignment.
With the deterioration of the bridge performance and ever-increasing amount of traffic, the bridge safety is becoming a concern for engineering community. A method that can assess the bridge&#039;s condition in real-time is urgently needed. The main factors that hinder the bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA) based method is developed for structural damage detection of bridges in operational environments in this paper. Two main operational environmental factors: the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate to assess the bridge condition in operational environments.
The expression of the inverse calculation of Fresnel diffraction has been derived and the definition of Fresnel diffraction transform is given. Based on Gerchberg-Saxton(GS)calculation method, Fresnel diffraction transform is applied to the design of binary optical elements and an example of optical element design is provided, which is used in the laser marking on the surface of the products.
A design method of asphalt mixture is proposed for reducing the tyre/road noise while having good mechanical properties. Firstly, by comparing the simulated sound absorption coefficient curve with noise-frequency curve from practical road surface, the target air voids contents are obtained. Then, models which can predict the texture level and sound absorption coefficient from the given mixture properties, such as porosity, gradation, particle size, asphalt content, etc. as inputs parameters, are used for preliminarily selecting the design parameters of mixture. Test samples are made according to these selected design parameters in the lab. Test vales of the texture level, sound absorption coefficient and the skid resistance of the mixtures are obtained from laboratory measurements. These measurement values are used for predicting the attenuation of the tyre/road levels by means of statistical models. Mixtures with superior predicted noise reduction properties are selected for mechanical performance validation. Mixture design which shows the promising noise reduction properties as well as satisfying the mechanical performance requirement are considered as the optimal one, and it is suggested to be used as the road surface. Road surface designed from this method can both meet the needs of traffic load and noise reduction function.
Global Navigation Satellite System (GNSS) is the broad name for satellite based navigation system and one of the application areas of this field is addressed in this contribution. This paper is drafted to present the design and simulation of GNSS phase based Attitude Determination System for small satellite. GNSS Phase based solutions are considered more reliable and accurate as compared to the code based solutions but the phase measurements demand some ambiguity resolution algorithm for the required accuracy. GNSS phase based attitude determination algorithm using Extended Kalman Filter (EKF) for attitude determination and LAMBDA method for ambiguity resolution are analyzed and implemented on the small satellite model. Three commercial-off-the-shelf GPS antennas are used for experimentation where one served as the master and two as the slave antennas. The RINEX data is used for the observations and navigation message and processed in the MATLAB functions to get the position of the satellite using the ephemerides and applying the navigation solution to get the position of the master antenna by using the method of single point positioning while incorporating the different errors like Tropospheric, Ionospheric and Clock errors. Base line is calculated by applying the Least Square Solution after resolving the ambiguity and performing the method of differential positioning using double difference measurements to find the slave antenna positions. Finally, extended Kalman filter is applied to get the attitude measurements that report to be accurate within one degree precision.
