Adaptive Kernel Density Estimation Method Research Articles

Robust adaptive analysis of dynamic responses of offshore sustainable energy systems

The state of the problem in the field of ocean engineering is that all the existing parametric or non-parametric models can not accurately and efficiently predict the sea state parameter probability distribution tails. Therefore, a research motivation has been developed to propose a new method to forecast in the tail region. In this paper the work objectives and scope are to propose a new adaptive kernel density estimation method formulated on the theory of linear diffusion processes and to utilize this method for predicting the sea state parameter probability distribution tails and for calculating the 50-year extreme Power-Take-Off heaving force value. The key findings are that our proposed new method is robust and can forecast well the 50-year extreme design force values for offshore sustainable energy systems.

A novel method for analysis of offshore sustainable energy systems

In the present research, we have proposed a new adaptive kernel density estimation method formulated on the theory of linear diffusion processes. By examining the calculation results we have found that in the tail region, our proposed new adaptive kernel density estimation distribution curve becomes very smooth and fits quite well with the histogram of the measured ocean wave dataset at the US National Data Buoy Center station 46026. Carefully studying the calculation results also reveals that the 50-year extreme Power-Take-Off heaving force value forecasted based on the environmental contour derived using the new method is 3021700N, which is much larger than the value 2458700N forecasted via the Rosenblatt-inverse second-order reliability method contour method. Consequently, our proposed new adaptive kernel density estimation method formulated on the theory of linear diffusion processes can forecast well and efficiently forecast the 50-year extreme design force values for offshore sustainable energy systems.

Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model

At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively.

Estimation and Reliability Research of Post-Earthquake Traffic Travel Time Distribution Based on Floating Car Data

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time.

A Kernel-Density based Semi-Parametric stochastic degradation model with dependent increments

The degradation modeling of highly reliable industrial products is a significant issue for manufacturers, and stochastic process models have been widely applied to model degradation trends. However, they suffer from the two underlying assumptions: the degradation increments following a specific parametric distribution and mutually independent degradation increments. Hence some degradation trends cannot be well captured by these models. In this paper, we propose a general semi-parametric stochastic degradation model to fit the degradation data. The probability density function of the degradation increments is estimated by the adaptive kernel density estimation method, and the copula function is used to measure the dependence of the successive degradation increments. Increments are extrapolated by marginal conditional distributions. A simulation study is carried out where the degradation increments are generated under five distributions, and four degradation models are used to fit the data. The simulation results show that the proposed model can well fit the data generated from the existing stochastic process models as well as other models. Finally, several real datasets are used to verify the validity of the proposed method, which can generate more similar degradation paths to the real ones and thus can provide a more accurate lifetime prediction.

AUTOMATIC CROWD ANALYSIS FROM VERY HIGH RESOLUTION SATELLITE IMAGES

Abstract. Recently automatic detection of people crowds from images became a very important research field, since it can provide crucial information especially for police departments and crisis management teams. Due to the importance of the topic, many researchers tried to solve this problem using street cameras. However, these cameras cannot be used to monitor very large outdoor public events. In order to bring a solution to the problem, herein we propose a novel approach to detect crowds automatically from remotely sensed images, and especially from very high resolution satellite images. To do so, we use a local feature based probabilistic framework. We extract local features from color components of the input image. In order to eliminate redundant local features coming from other objects in given scene, we apply a feature selection method. For feature selection purposes, we benefit from three different type of information; digital elevation model (DEM) of the region which is automatically generated using stereo satellite images, possible street segment which is obtained by segmentation, and shadow information. After eliminating redundant local features, remaining features are used to detect individual persons. Those local feature coordinates are also assumed as observations of the probability density function (pdf) of the crowds to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf which gives us information about dense crowd and people locations. We test our algorithm usingWorldview-2 satellite images over Cairo and Munich cities. Besides, we also provide test results on airborne images for comparison of the detection accuracy. Our experimental results indicate the possible usage of the proposed approach in real-life mass events.

Feature analysis for detecting people from remotely sensed images

We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using back- ground information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algo- rithm in real-life mass events. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.7.073594)

KALMAN FILTER BASED FEATURE ANALYSIS FOR TRACKING PEOPLE FROM AIRBORNE IMAGES

Abstract. Recently, analysis of man events in real-time using computer vision techniques became a very important research field. Especially, understanding motion of people can be helpful to prevent unpleasant conditions. Understanding behavioral dynamics of people can also help to estimate future states of underground passages, shopping center like public entrances, or streets. In order to bring an automated solution to this problem, we propose a novel approach using airborne image sequences. Although airborne image resolutions are not enough to see each person in detail, we can still notice a change of color components in the place where a person exists. Therefore, we propose a color feature detection based probabilistic framework in order to detect people automatically. Extracted local features behave as observations of the probability density function (pdf) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf. First, we use estimated pdf to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in non-crowd regions automatically for each image in the sequence. We benefit from Kalman filtering to track motion of detected people. To test our algorithm, we use a stadium entrance image data set taken from airborne camera system. Our experimental results indicate possible usage of the algorithm in real-life man events. We believe that the proposed approach can also provide crucial information to police departments and crisis management teams to achieve more detailed observations of people in large open area events to prevent possible accidents or unpleasant conditions.

TH-C-T-617-01: A Proposed Alternative to Phase-Space Recycling Using Adaptive Kernel Density Estimator

Purpose: To investigate the adaptive kernel density estimation (AKDE) method as an alternative to recycling phase-space (PS) files or histogram binning during MC accelerator simulation. The AKDE approach has the potential to mitigate statistical “noise” issues (due to latent variance) in the PS data, without the need for an overwhelmingly large number of particles. Method and Materials: We have implemented a nonparametric density estimation technique, the AKDE method, to generate additional PS variables in the vicinity of simulated PS points in MC accelerator simulation. The method involves the placement of kernels at simulated PS points, and the window-width is allowed to vary based on the density of the PS points. After successfully testing the method for sampling 1-D and 2-D exponentials we sampled PS files generated from accelerator simulations. The original PS (x,y,u,v,E) was reduced to a rotationally invariant PS (r,θα,E) assuming azimuthal symmetry above the collimating jaws. The new PS point (r',θ',α',E') is generated by sampling in the vicinity of (r,θ,α,E) To test the method we simulated dose profiles for a 10×10cm2 field using 1×106 particles in the PS file as input. Results: Results indicate that a minimum number of PS points are needed to allow accurate density estimation. Preliminary calculations using a global window-width for each PS variable produce relatively smooth profiles even with as little as 10 million particles in the PS file. However, profiles with the Epanechnikov kernel appear to be smoother and in better agreement with the original PS versus either Gaussian or uniform kernels. Conclusion: By generating unbiased PS samples “on-the-fly” in the neighborhood of simulated PS points AKDE has been shown to be a promising alternative to PS recycling or histogram-binning (source modeling), where binning effects and propagation of systematic latent uncertainties may pose potential problems.