An Improved Gaussian Mixture Model-Based Data Normalization Method for Removing Environmental Effects on Damage Detection of Structures

Because of its complexity, the Gaussian mixture model algorithm costs many computing resources (CPU cycles) and it is difficult to realise real-time moving object detection on the embedded system. A real-time motion detection method based on an improved Gaussian mixture model is presented in this paper, which is optimised and adjusted for the Gaussian mixture model. It is also optimised on C language and CPU for running on an embedded system. The design of software and hardware on DM6446 is presented in detail including the implementation of the algorithm. The experimental results show that the system can effectively overcome the interference from external environment and realise multi-object real-time motion detection and tracking.

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Effects of environmental and operational actions on the modal frequency variations of a sea-crossing bridge: A periodicity perspective

In this paper, we propose a novel rural construction land extraction algorithm for Unmanned Aerial Vehicle images using an improved Gaussian mixture model. Firstly, in the Gaussian mixture model, instead of mixed probability of various types of surface features, we calculate the prior probability of the various features in the neighborhood of each pixel using Markov random field. It can reflect the features' spatial correlation. Secondly, we use the simulated annealing to obtain the global optimum parameter estimates in the process of parameter estimation. Finally, we calculate the posterior probability of each pixel for the features using the parameters' estimated value. Then, we can obtain the spatial distribution of various features. The effect of the proposed algorithm is analyzed through experiment. The experiment shows that our proposed method can improve accuracy of construction land information extraction and has better performance than other methods.

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation.

The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

This paper first introduces the principle of target detection based on Gaussian mixture model, and then introduces the improved method of the original Gaussian mixture model to detect moving targets in detail. The main content is the improvement of initialization method. In this paper, the fast initialization method is used, and the learning rate in the training stage is adjusted, so that it can quickly adapt to the changes in the environment in the training stage. In the aspect of parameter updating, the concept of change rate is introduced, which aims to describe the size of changes in the current frame and the previous frame, so as to adjust the learning rate adaptively In terms of the total number of Gaussian components, this paper adopts the method of initializing a single Gaussian model to increase the Gaussian components with the complexity of description, and the method of combining the removal of Gaussian components and the combination of the removal of Gaussian components in each frame. The removal of Gaussian components can directly remove the Gaussian components with small weight and reduce unnecessary calculation, while the combination of Gaussian components can reduce calculation to describe the background more precisely. In the last part of the improvement, the frame difference method is combined with the Gaussian mixture model to make the edge detection more accurate. Finally, a GUI interface is designed.

Efficient wind energy harvesting becomes more and more important as a consequence of the increasing interest in renewable energy in the European Union [1]. This leads to growing sizes of wind turbines (WTs), and with it, larger WT blades (WTBs). The structural designs of these WTBs are created to optimize the potential energy output, where low mass is a key requirement. However, high flexibilities and lower buckling capacities are further results of these developments [2], thus certain damage scenarios become significant. Intelligently designed structural health monitoring (SHM) systems can help to reduce the associated operations and maintenance costs. Even though, several techniques are already developed for structural damage detection (SDD) in WTBs, the majority of these methods is not suitable for inservice measurements. This paper presents a SDD and structural damage localization (SDL) method based on the partial autocorrelation function (PACF) of vibration responses. The approach is applied to a numerical model of a large WTB, where the acceleration responses are obtained from transient dynamic simulations with a simplified aerodynamic loading approach. The novel damage sensitive feature (DSF) is developed as the Mahalanobis distance between a baseline and current vector of PACF coefficients. First, numerical modal analysis of the finite element (FE) WTB model is performed in order to estimate the effect of a disbonding damage scenario on the vibration characteristics. Second, the behaviour of the PACF for time series of the healthy system is discussed. Third, the SDD results on the basis of statistical hypothesis testing are assessed for two selected sensor locations and increasing damage extents. Finally, the performance of the proposed DSF with respect to SDL is illustrated for multiple locations on the WTB’s surface. This study demonstrated the efficiency of a DSF based on the PACF for SDD and SDL, which is promising for future developments of vibration-based SHM techniques in WTBs.

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

This research proposed and verified an innovative method to identify and locate structural damage using only the response of operational vibration, that is the displacement acquired by a non-contact optical method. The most efficient and economical way to detect damage within the structure is to monitor its structural health while in operation. However, the uncertainties and the randomness of ambient vibrations due to the operation and environments cause a challenge in conducting the operational analysis. Current technology limits the ability to collect data on the properties of the structure without the interruption of operation. Frequencies and mode shapes have been widely used in structural damage detection, but they are not sensitive enough and cannot provide sufficient information for identifying damage locations and their quantification. Therefore, the goal of this research is to design and verify a method to detect the damage, as well as its location and severity, of structures in operation without any physical contacts for data acquisition (i.e., non-contact based structural health monitoring (SHM)). Three algorithms are integrated into this SHM process. The first algorithm is the determination of structural characteristics (frequencies and mode shapes) of a vibrating structure from output-only data. Stochastic Subspace Identification (SSI) method is applied to measured displacements over time to extract the structural characteristics. The second algorithm is to estimate the scaling factor. The mode shapes obtained from the output-only model analysis are unscaled due to the absence of the information of input excitation forces. Mass Change Modal Scale (McMS) algorithm is used to estimate the modal scaling factors and determine the scaled mode shapes. The third algorithm is to estimate the structural system matrices (i.e., mass and stiffness matrices) and assess the damages. A Finite Element Model Updating (FEMU) is applied and the system matrices are updated from frequencies and scaled mode shapes. The damage within the structure can then be detected by analysing changes in mass and stiffness matrices. All three phases are verified by numerical simulation and laboratory experiments with deflections acquired by non-contact optical methods through video system. At last, to achieve the non-contact based SHM, a modal scaling method based on temperature change is proposed and verified by numerical simulation. Experimental program reveals that the proposed algorithm using McMS method is applicable to detect damage locations and their mass losses. With proposed non-contacted based SHM, the limitations of contact based sensor can be addressed, and the structural damage

For elderly people of living alone, falls and consequent resulting physical-psychological injuries even injury-related deaths are a major health hazard. How to analyze and utilize the captured videos by public places based on image processing is one hot research topic. Abnormal fall detection consists of four parts, which are captured video pre-processing, moving object detection, fall behavior recognition and experimental analysis. In this paper, dilation and corrosion, Gaussian filtering and median filtering are adopted to complete frame image sequences preprocessing to remove noises. Then, an improved background subtraction method based on Gaussian mixture model is used to detect moving objects. Based on extracted trajectory feature and shape feature of foreground objects, three-level hierarchical detectors are utilized to complete fall behavior recognition. At last, experimental analysis is done based on testing the system performance. Image processing speeds of our system is 15 frames per second if the lights are stable, which meets the requirements of real-time detection. Moreover, both the moving objects real-time detection rate and falling behavior detection accuracy rate are more than 95% in our experiments.

As it is difficult to detect small moving targets in wide field of view and long distance environment, this study uses the fluctuation of optical field intensity to enhance the characteristic signal of moving targets, and then combines the local Gaussian mixture model to segment the foreground of the image enhancement area. The detection of small moving targets in different long-distance and complex backgrounds is realized. The result indicates that: The detection algorithm in this study can effectively improve the detection rate of small motion with long distance, wide field of view and low signal-to-noise ratio. The detection rate of drones photographed at 200 meters, 400 meters and 700 meters was 82%.

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

For the traditional Gaussian mixture model has the shortages of low convergence rate and being sensitive to sudden light change, a nearest neighbor n-frame improved algorithm based on chroma space is presented. On one hand, when luminance has an abrupt change, the objects can be extracted by using chroma information of every pixel instead of RGB, so that the disturbance for the detection resulting from sudden light change can be avoided effectively; on another hand, for accelerating the convergence rate and reflecting the dynamic changes of the background in time, the improved algorithm needs to record the matching of Gaussian distributions with measured values in recent n frames, and use the exponential function of matching times to update the weight of models. The results of experiments prove that the improved algorithm proposed in this paper has better convergence and robustness than that of the traditional one.