Density Estimation Technique Research Articles

Whole Process Monitoring Based on Unstable Neuron Output Information in Hidden Layers of Deep Belief Network.

Process monitoring based on deep learning has attracted considerable attention. Generally, several hidden layers exist in the deep-learning model, and only the output information of the last hidden layer neurons extracted by deep learning is applied. Considering that each hidden layer is a kind of information representation of the original data, the information of different hidden layers may contain positive elements for process monitoring. In this article, we found that when a fault occurs, there are some neurons in each hidden layer that the information they output are different, compared with the normal condition. These neurons are called unstable neurons. Obviously, the information they output are beneficial for process monitoring. Motivated by theoretical analysis and experimental studies on unstable neurons, a novel method (UN-DBN) based on the unstable neurons in hidden layers is proposed to integrate the useful information for process monitoring, the Euclidean metric, the moving average filter, and the kernel density estimation technique are employed to provide an intuitionistic expression of the working state. The comparable result applied on a mathematic simulation process and the TE process with other advanced monitoring methods confirms the superiority and feasibility of the proposed method UN-DBN in this article.

Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter

In this paper, a novel hybrid control method is proposed to enhance the tracking performance of the Proportional–Integral (PI) based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. The system performance is presented by tracking error entropy as the system is nonlinear and subjected to non-Gaussian noises. The well-known kernel density estimation (KDE) technique is employed to estimate the entropy because the precise statistical property of noises is not available for many industrial processes. Since in many industrial cases gains of PI controllers are fixed, a compensative controller is designed without changing the existing closed loop PI controller. Moreover, the compensative signal is formed using the estimated states from the extended Kalman filter (EKF) and a nonlinear compensation realized by the radial basis function (RBF) neural network. The weights of RBF are trained to minimize the entropy of the closed loop tracking error. The convergence of RBF network is discussed and the stability of the resulting closed-loop control system is analysed in mean square sense. Finally, two numerical examples and a practical system simulation are given to illustrate the effectiveness of the proposed control method.

Benefits of noise in M-estimators: Optimal noise level and probability density

For the robust estimation of a location parameter, we consider a parallel array of maximum likelihood type estimators (M-estimators). We investigate the possibility of added noise as a design variable of the M-estimators, and characterize a nonzero optimal amount of added noise maximizing the efficiency for estimation. The added noise shows its benefits to the asymptotic efficiency of the M-estimator when the noise level and the noise probability density are optimally tuned. The optimal noise level can be theoretically derived by maximizing the asymptotic efficiency as the probability density of added noise is given. Based on the Parzen-window density estimation technique, we approximate the infinite-dimensional non-convex optimization of the optimal probability density of added noise as a simpler optimization problem with respect to a finite-dimensional vector under certain constraints. This approximate solution for the optimal probability density of added noise shows its feasibility for various M-estimators with an arbitrary array size, which is also validated by simulation results.

Improved trajectory similarity-based approach for turbofan engine prognostics

Trajectory similarity-based prediction (TSBP) is an emerging real-time remaining useful life (RUL) prediction method that has drawn considerable attention in the field of data-driven prognostics. TSBP is fast, and the corresponding model is easy to train. However, TBSP only provides a point estimation of RUL, which is insufficient for some specific prognostic applications. Hence, this study introduces an improved TSBP method to handle the issue of prognostic uncertainty. On the basis of an adaptive kernel density estimation technique and β-criterion, the improved TSBP method not only provides an accurate and precise point prediction of RUL but also specifies the confidence interval of RUL prediction. The capability of obtaining the confidence interval of RUL can enhance the TSBP method for uncertainty management. The effectiveness of the proposed method is validated through two cases studies, which are related to turbofan engine prognostics.

Density estimation techniques for multiscale coupling of kinetic models of the plasma material interface

In this work we analyze two classes of Density-Estimation techniques which can be used to consistently couple different kinetic models of the plasma-material interface, intended as the region of plasma immediately interacting with the first surface layers of a material wall. In particular, we handle the general problem of interfacing a continuum multi-species Vlasov-Poisson-BGK plasma model to discrete surface erosion models. The continuum model solves for the energy-angle distributions of the particles striking the surface, which are then driving the surface response. A modification to the classical Binary-Collision Approximation (BCA) method is here utilized as a prototype discrete model of the surface, to provide boundary conditions and impurity distributions representative of the material behavior during plasma irradiation. The numerical tests revealed the superior convergence properties of Gaussian Mixture Models over Kernel Density Estimation methods, with Gaussian Mixtures and Epanechnikov-KDEs both being up to two orders of magnitude faster than Gaussian-KDEs. The methodology here presented allows a self-consistent treatment of the plasma-material interface in magnetic fusion devices, including both the near-surface plasma (plasma sheath and presheath) in magnetized conditions, and surface effects such as sputtering, back-scattering, and ion implantation. The same coupling techniques can also be utilized for other discrete material models such as Molecular Dynamics.

Identification of differential risk hotspots for collision and vehicle type in a directed linear network

Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved.In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014–2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern.A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.

Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped

Crowd control is needed to prevent the outbreak of disorder and prevent possible stampedes. An automated detection of people crowds from images has become a very important research field. Due to the importance of the topic, many researchers tried to solve this problem using CCTV street cameras. There are still significant problems in managing public pedestrian transport areas such as railway stations, stadiums, shopping malls, and religious gatherings. Using CCTV cameras, some image processing techniques are suitable for an automatic crowd monitoring system. The feasibility of such a system has been tested by analyzing the crowd behavior, crowd density and motion. Traditional measurement techniques, based on manual observations, are not suitable for comprehensive data collection of patterns of density and movement. Real-time monitoring is tedious and tiring, but critical for safety. The author has investigated a number of techniques for crowd density estimation, movement estimation, incident detection and their merits using image processing.

Reliability analysis of stochastic structure with multi-failure modes based on mixed Copula

In view of that there exist complicated correlations among failure modes of the stochastic structure because of the same uncertain variables that are contained in different failure modes, many reliability analysis methods based on empirical probability model have been developed to calculate and assess the structural reliability. However, the empirical distributions of uncertain variables are not always available or not accurate. Thus, based on the available sample data of random variables of the stochastic structural system to be analyzed, this paper presents a new reliability analysis method in which the mixed Copula is constructed to describe the correlations among multi-failure modes. First, the Gumbel Copula and Clayton Copula, which have the upper and lower tail dependence separately, are utilized to construct a mixed Copula so as to describe the correlations among multi-failure modes. Then, the sample data of the random variables is substituted into the performance functions of failure modes to calculate the sequence values thereof, and the probability density function (PDF) and cumulative distribution function (CDF) of each performance function of the structure are estimated by using the nonparametric kernel density estimation (NKDE) technique. Subsequently, the Canonical Maximum Likelihood (CML) method is applied to estimate the correlation parameters in the mixed Copula. Finally, the main girder structure of an overhead traveling crane is investigated by using the proposed method, and the results indicate that the new reliability analysis method is effective and valid. Compared with traditional reliability analysis methods, the new method, in which the available sample data of random variables is directly used to calculate the values of performance functions so as to avoid the errors caused by empirical distributions, provides some important theories and engineering contributions for the reliability design analyses and safety assessments of actual engineering structures, especially these with complex correlations among multi-failure modes.

Long-term archaeological perspectives on new genomic and environmental evidence from early medieval Ireland

Using archaeological data, this paper investigates past population trends in Ireland as a response to recent genomic studies that have identified admixture signals in the genomes of Irish people caused by historically-recorded migration events. Among these was Norse settlement in the 9th-10th Centuries CE, which has a greater than expected signal in the contemporary population of the island. Here, we contextualise these discoveries using a large database of recently discovered archaeological sites with radiocarbon dates that we have analysed using Kernel Density Estimation techniques. We argue that the Viking migrations occurred following a 300-year period of population decrease in Ireland. This new, data-driven synthesis of the archaeological record contrasts with previous accounts of early medieval Ireland as a period of ever-growing expansion and progression. However, this new interpretation is also aligned to evidence for economic and environmental change, including recent discoveries concerning the soil nitrogen cycle and agricultural intensification. We compare historical evidence for Viking migrations to later episodes of migration between Britain and Ireland, where more details are known about the size of the incoming groups, ultimately wishing to confront the opinion that past population sizes cannot be fathomed for cultures without documentary records. Through comparison with historic analyses and census records, we make broad estimates of absolute population size in Ireland since prehistoric times, including during these demographic events, and argue that much value is added to genomic evidence for migration when these points in time are contextualised in terms of evolving population trends.

Application of Image Processing and Data Mining Techniques for Traffic Density Estimation and Prediction

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

Combining LULC data and agricultural statistics for A better identification and mapping of High nature value farmland: A case study in the veneto Plain, Italy

High Nature Value farmland (HNVf) are characterised by high naturalness of land cover and low intensity of agricultural practices. They are essential for biodiversity conservation in rural environments, and their presence is an important indicator of the effectiveness of different EU policies that aim to support biodiversity in agricultural areas. Consequently, their identification, protection and implementation is of strategic importance. Previous studies have analysed the landscape along an urban-rural-natural gradient, beginning with Land Use and Land Cover (LULC) maps. Building up from this baseline, this paper integrates spatial analysis methods with statistical data related to agricultural practices and their intensity, with the aim of mapping and assessing HNVf in a portion of the Veneto Plain, north-east Italy. In particular, this paper presents a methodology for the identification of HNVf applied to two datasets: (i) the first encompassing only LULC data and (ii) the second encompassing also statistical data on agricultural practices. The aim is to demonstrate how this additional information improves the identification of HNVf.In the first step, a Kernel Density Estimation (KDE) technique is applied to a reclassified LULC map, in order to calculate continuous intensity indicators. A Principal Components Analysis and an ISODATA Cluster Analysis are then performed respectively to remove redundant information and to identify the different landscape structures of the study area. The second analysis follows the same steps, with the difference that LULC intensity indicators are analysed in combination with data on crop rotations, irrigation and livestock from a census survey.The first analysis returns a map of the landscape driven only by different intensities of land use. The second returns a map where the statistics on agricultural practices allow for a better characterisation of the natural value of the landscape. Agricultural statistics improved the results, since they allow the discrimination of lower intensity clusters within the cultivated areas, which are traditionally excluded from HNVf by considering only the land cover. The comparison between the results of the two analyses shows that the combined use of the agricultural statistics determines a more detailed representation of the study area, that allows a better differentiation of the agricultural areas between HNVf and non-HNVf, leading to an improvement of the HNVf identification methodology. The benefit of using additional information can be therefore of interest for territorial planning, with the ultimate aim of promoting biodiversity conservation.

Likelihood-based estimation of a semiparametric time-dependent jump diffusion model of the short-term interest rate

This paper proposes a semiparametric time-dependent jump diffusion model in an effort to capture the dynamic behavior of short-term interest rates. The newly proposed model includes a wide variety of well-known interest rate models, incorporating the time-varying instantaneous return, volatility as well as jump component. The local likelihood density estimation technique together with pseudo likelihood estimation method is employed to estimate the parameters of the model. Some simulations are conducted to examine the statistical performance of our estimators. The proposed procedure is then applied to analyze daily federal funds rate.

Covariate Information Matrix for Sufficient Dimension Reduction

ABSTRACT Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regression function. CIM is implemented via eigen-decomposition of a matrix estimated with a previously developed efficient nonparametric density estimation technique. We also propose a bootstrap-based diagnostic plot for estimating the dimension of the CS. Results of simulations and real data applications demonstrate superior or competitive performance of CIM compared to that of some other SDR methods. Supplementary materials for this article are available online.

Crowd density estimation in still images using multiple local features and boosting regression ensemble

Crowd density estimation is a challenging research problem in computer vision and has many applications in commercial and defense sectors. Various crowd density estimation methods have been proposed by researchers in the past, but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical implementation. In this paper, we propose a fine-tuned and computationally economical, ensemble regression-based machine learning model for crowd density estimation. The WorldExpo’10 dataset has been used for experimental analysis and model performance evaluation. We extract variety of features in texture-based features such as gray-level co-occurrence matrix, local binary pattern and histogram of oriented gradients, structure-based features such as perimeter pixel and the orientation of pixels, and segment-based handcrafted features from each patch of the image and use an optimum combination of these features as input to the regression model. To achieve optimized memory utilization and faster speed, principal component analysis is employed to reduce the dimensions of the lengthy feature vector. Extensive experiments on different fronts ranging from the model hyperparameter optimization, features optimization and features selection were conducted, and at each step, we selected the most favorable results as input to the optimized model. The performance of the model is evaluated based on two popular metrics, i.e., mean absolute error and mean squared error. The comparative analysis shows that the proposed system outperforms the former methods tested on the WorldExpo’10 dataset.

A fast background model using kernel density estimation and distance transform

Background modelling is a key factor for foreground detection, which is imperative for people counting in a dynamic environment. In this paper, a background model with improved fast kernel density estimation technique while adopting distance transform is proposed for people counting. The kernel density estimation background model is improved by early-break method and LUT. Distance transform adopted is used to separate people who are closed together as one blob. It has been tested in a 2.6 GHz Intel Core computer with 25 fps on 432 × 240 images without code optimisation. The result shows that the improved method can be used in real-time processing and has a good result.

Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant

Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.

A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

A public transport journey planning service often yields multiple alternative journeys plans to get from a source to a destination. In addition to journey preferences, such as connecting time and walking distance, passengers can select the optimal plan based on mobile crowdsourced WiFi coverage available along the journey. This requires discovering mobile crowdsourced WiFi services available along the journey path. However, this task is challenging due to the uncertain availability of discovered services. To enhance the availability of WiFi coverage, we propose a probabilistic approach to discover groups of available crowdsourced WiFi services along with the journey segments. We first analyze the log of their trajectories and use a density estimation technique to discover reference spots representing the frequently visited locations. Then, a joint discrete Fourier transform and autocorrelation analysis are applied to mine the periods of the presence of moving crowdsourced services with respect to each reference spot. A low-complexity cluster analysis based on Jensen–Shannon divergence is then used to mine the periodic movement behaviors of services during the identified periods. Finally, mobile crowdsourced WiFi services that are simultaneously available at intersecting reference spots are grouped. The QoS of discovered groups is computed in terms of availability confidence, failover capacity, aggregated bandwidth capacity, and coverage. Additionally, we propose an algorithm to determine the best public transport journey plan offering based on the QoS of available WiFi service groups along the journey path. We conduct a comprehensive comparative study to validate the effectiveness of the proposed framework.

Layer-by-Layer Enhancement Strategy of Favorable Features of the Deep Belief Network for Industrial Process Monitoring

Recently, owing to its powerful feature extraction capability, deep learning has been applied to the field of fault detection, in which fault information is reflected in data fluctuation. Deep learning networks transmit fluctuation to the abstract feature layer, and the running state is determined on the basis of the fluctuation of the feature layer. Adjacent layers are connected by weights, whereas the weight value determines the amplification or reduction of input data fluctuation. Thus, abnormal fluctuation information may vanish during layer-by-layer feature extraction and this condition is detrimental to fault detection. In this study, to avoid such an event, the abnormal fluctuation information extracted by the deep belief network is monitored and enhanced layer by layer. Enhancement degree is determined by the fluctuation degree, and the enhanced features are integrated via support vector data description, moving average filter, and kernel density estimation techniques to visualize the current work...

A prediction-Based VM consolidation approach in IaaS Cloud Data Centers

Recent years have witnessed a rapid growth in exploiting Cloud environments to host and deliver various types of virtualized resources as on-demand services. In order to optimally use Cloud resources, the arrangement of virtual machines (VMs) in physical machines (PMs) must be performed strategically, because the placement of VMs in accordance with the available resources can reduce energy consumption, improve resource utilization and, consequently, can increase companies benefits. However, VMs could have time varying workloads, which leads to degradation of performance and power consumption. Thus, re-configuring the VMs placement is essential. Virtual machine consolidation aims to optimally use the available resources by allocating several virtual machines on a set of physical ones (PMs). To determine the PMs capacities to reallocate VMs, it is important to predict their states based on resource utilization history within each VM, and the past VMs migration traffic. However, a common limitation between existing VM consolidation approaches is the lack of information about the history of (and the future) VM migration traffic. Through this paper, we aim to propose a virtual machine consolidation approach based on the estimation of requested resources and the future VM migration traffic. We exploit the strength of Kernel Density Estimation technique (KDE) as a powerful mean to forecast the future resource usage of each VM, and AKKA toolkit as an actor-based model that allows exchanging useful information about the host’s states. We adopt a weighted-graph representation to model the history of migration traffic between PMs and to design the actor-based topology of the data center. The obtained results show the effectiveness of our approach in terms of total number of migrations and energy consumption.