An Improved Analytical Methodology for Joint Distribution in Probabilistic Load Flow

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.

In structural health monitoring, effectively eliminating the influence of variable environmental conditions on modal frequencies remains a critical challenge for accurate damage identification. Nonstationary and nonlinear variations in modal frequencies, commonly induced by environmental changes, tend to overshadow the effects caused by structural damage. An improved Gaussian mixture model (GMM) is proposed in this paper to normalize nonlinear and nonstationary frequency data, enabling effective structural damage detection under variable environmental conditions. As the effectiveness of the GMM is highly influenced by the initial parameter values used in the expectation-maximization (EM) algorithm, a subdomain division strategy is first presented to determine the unique initial values of the GMM parameters. Through the application of the EM algorithm, the GMM is constructed simply and efficiently through the determined initial parameters. Next, on the basis of the constructed GMM, the modal frequency data are normalized to extract damage features that remain unaffected by environmental variations. Subsequently, Hotelling’s T2 statistic and its cumulative form are calculated for the damage features and designated as the damage indicators; meanwhile, the corresponding damage thresholds are also calculated according to the kernel density estimation technique. To validate the proposed method, two case studies are conducted: one with a numerical mass-spring system and the other with a real bridge structure. Results show that environmental influences no longer impact the normalized frequency data, and the cumulative statistic demonstrates outstanding accuracy in identifying structural damage.

Normal-transformation-based probabilistic load flow with correlated wind and load forecast errors

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.

Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze transmission system behavior, has been studied for decades. Despite a variety of available methods, existing PLF approaches rarely take system control into account. However, system control, as an automatic buffer between the fluctuations in random variables and the variations in system variables (e.g., nodal voltages), has a significant impact on the final PLF result. To consider control actions' influence, this paper proposes the first analytical PLF method for the transmission grid that takes into account primary and secondary frequency controls. This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach. This paper also proves that if the joint probability distribution (JPD) of random variables is expressed by a Gaussian mixture model (GMM), then the JPD of system variables is an infinite GMM. By leveraging this proposition, the proposed method generates the joint PLF of the whole system at the quasi-steady and steady states. The high accuracy and satisfactory efficiency of this method are verified on multiple test systems with different percentages of renewables.

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.

This paper proposes a moving target detection scheme suitable for camera motion. Firstly, the background model is initialized by a Gaussian mixture model algorithm. Then Kanade-Lucas-Tomasi Feature Tracker (KLT) method is used to detect optical flow feature points of two adjacent frames, RANdom SAmple Consensus (RANSAC) algorithm is used to filter out the correct matching points and obtain a homography matrix, which can recover the background model matching the current frame, the new background model is used to detect moving target of the current frame. In the foreground detection stage, the current pixel is first compared with its own background model, and then compared with the background model of its 8 neighborhood pixels, the algorithm is speeded up without reducing the detection accuracy in this way; In the update stage of the background model, in order to adapt to the background changes caused by camera motion, an age value variable is set for each pixel. The experimental results show that the improved algorithm has a significant improvement in detection accuracy and running time compared to Gaussian mixture background modeling.

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.

This paper presents various motion detection methods: temporal averaging (TA), Bayes decision rules (BDR), Gaussian mixture model (GMM), and improved Gaussian mixture model (iGMM). This last model is improved by adapting the number of selected Gaussian, detecting and removing shadows, handling stopped object by locally modifying the updating process. Then we compare these methods on specific cases, such as lighting changes and stopped objects. We further present four tracking methods. Finally, we test the two motion detection methods offering the best results on an object tracking task, in a traffic monitoring context, to evaluate these methods on outdoor sequences.KeywordsComputer visionMotion detectionObject tracking