Kernel Density Estimation Technique Research Articles

Dynamic economic emission dispatch with wind power based on improved multi‐objective brain storm optimisation algorithm

Based on the non-parametric kernel density estimation technique, the probability distribution of wind power output and its forecast error are accurately modelled. The confidence interval and forecast error upper and lower bounds of wind power output are estimated to build a dynamic economic emission dispatch (DEED) model with wind power penetration. To effectively solve the DEED problem with multi-objective, high-dimensional, non-linear and strong constraints, based on the basic brainstorm optimisation (BSO) algorithm, three improvement mechanisms, namely random clustering centre, differential mutation operation and individual crossover operation are introduced to enhance the converging and diverging operation of BSO. Based on these improvements and external archive mechanism, an improved multi-objective BSO (IMOBSO) algorithm is proposed. Simulations on a classical test system with ten thermal units are performed, where two case studies are investigated carefully. The simulation results demonstrate that: (i) the proposed IMOBSO can optimise the cost and emission objectives simultaneously and have achieved better performance than other algorithms; (ii) the proposed DEED incorporating wind power model is reasonable and effective because it can achieve the optimal wind power output scheduling by adjusting the system's spinning reserve capacity and the confidence level of wind power prediction interval.

Crowdsourcing Data for the Elaboration of Noise Maps: a Methodological Proposal

In recent decades, the awareness that noise pollution caused by traffic, industry and recreational activities constitutes one of the main environmental problems is growing. In order to control the environmental noise, many regulations propose the creation of noise maps according to standard procedures that involves the data acquisition, analysis and elaboration. In this paper, crowdsourcing noise data collection is described. It involves volunteers that can record sound pressure levels thanks to specific applications on their mobile devices, such as the “NoiseCapture” app developed in France by CNRS and IFSTTAR, and upload the measurement, with their GPS location data, on a continuously updated map. This approach mainly contributes to citizens’ greater awareness about noise pollution in an urban area. Beside this mode of data acquisition, the paper focuses on the analysis of the acquired data with the kernel density estimation technique, implemented in a GIS environment. The results allows the elaboration of sound density maps, defined from the spatial and temporal point of view, that can support the appropriate mitigation actions.

Performance-based wind design of tall buildings: concepts, frameworks, and opportunities

One of the next frontiers in structural wind engineering is the design of tall buildings using performance-based approaches. Currently, tall buildings are being designed using provisions in the building codes and standards to meet an acceptable level of public safety and serviceability. However, recent studies in wind and earthquake engineering have highlighted the conceptual and practical limitations of the code-oriented design methods. Performance-based wind design (PBWD) is the logical extension of the current wind design approaches to overcome these limitations. Towards the development of PBWD, in this paper, we systematically review the advances made in this field, highlight the research gaps, and provide a basis for future research. Initially, the anatomy of the Wind Loading Chain is presented, in which emphasis was given to the early works of Alan G. Davenport. Next, the current state of practice to design tall buildings for wind load is presented, and its limitations are highlighted. Following this, we critically review the state of development of PBWD. Our review on PBWD covers the existing design frameworks and studies conducted on the nonlinear response of structures under wind loads. Thereafter, to provide a basis for future research, the nonlinear response of simple yielding systems under long-duration turbulent wind loads is studied in two phases. The first phase investigates the issue of damage accumulation in conventional structural systems characterized by elastic-plastic, bilinear, pinching, degrading, and deteriorating hysteretic models. The second phase introduces methods to develop new performance objectives for PBWD based on joint peak and residual deformation demands. In this context, the utility of multi-variate demand modeling using copulas and kernel density estimation techniques is presented. This paper also presents joined fragility curves based on the results of incremental dynamic analysis. Subsequently, the efficiency of tuned mass dampers and self-centering systems in controlling the accumulation of damage in wind-excited structural systems are investigated. The role and the need for explicit modeling of uncertainties in PBWD are also discussed with a case study example. Lastly, two unified PBWD frameworks are proposed by adapting and revisiting the Wind Loading Chain. This paper concludes with a summary and a proposal for future research.

Move to nocturnality not a universal trend in carnivore species on disturbed landscapes

Anthropogenic landscape change is a leading driver of biodiversity loss. Preceding dramatic changes such as wildlife population declines and range shifts, more subtle responses may signal impending larger‐scale change. For example, disturbance‐induced shifts to species’ activity patterns may disrupt temporal niche partitioning along the 24‐h time axis, compromising community structure via altered competitive interactions. We investigated the impacts of human landscape disturbance on species’ activity patterns and temporal niche partitioning in the Canadian Rocky Mountain carnivore guild using camera trap images collected across two regions encompassing a wide gradient of human footprint. Applying kernel density estimation techniques, we tested for carnivore species’ activity shifts 1) between a low versus high disturbance landscape, and 2) in relation to site‐scale disturbance. To test our hypothesis that human disturbance impacts species’ temporal niche partitioning, we modelled activity overlap between co‐occurring carnivore species in relation to natural and anthropogenic landscape features, as well as carnivore community composition. Multiple carnivore species altered activity patterns between the low versus high disturbance landscapes and camera sites, but these shifts varied considerably among species. While wolves appeared to increase nocturnal activity in relation to disturbance, coyote activity consistently trended towards cathemerality and marten increased diurnal activity. Detecting effects of landscape disturbance on activity overlap between co‐occurring species was highly sensitive to site‐level detection sample sizes, and our results suggest altered temporal niche partitioning between marten and wolverine in relation to forest cover. This study indicates that mesocarnivores may respond differently and perhaps indirectly to anthropogenic disturbance compared to apex predators. Apex predator shifts to nocturnality may facilitate a ‘behavioural release’ in mesocarnivores. This may be a likely component of mesocarnivore population release, with important management implications for ecological communities on disturbed landscapes.

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

Spatiotemporal Clustering and Analysis of Road Accident Hotspots by Exploiting GIS Technology and Kernel Density Estimation

Abstract Traffic accidents are a common problem in any transportation network. Road traffic accidents are predicted to be the seventh leading cause of deaths by the year 2030. Recently research in the integration of geographical information systems (GIS) for analyzing accidents, road design and safety management has increased considerably. The perpetual use of GIS tools, lead this study to propose the identification of accident hotspots by exploiting GIS technology coupled with kernel density estimation (KDE). This paper proposes the use of KDE technique and GIS technology to automatically identify the accident hotspots using UK as the study area. Analysis shows that most of the accidents occur when there is a 30 mph speed limit, a weekend, in the evening time, during the months of October and November, on the single carriageway, where there is ‘T’ or staggered junction and on ‘A’ road class. Moreover, this study also proposed techniques to classify the accident severity that is classified as either fatal, serious or slight. The driver behavior and environmental features achieved an accuracy up to 85% on the severity classification with Bagging technique. Further, the shortcomings, limitations and recommendations for future work are also identified.

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. To this end, we develop two machine-learning-based approaches. Our first approach adopts a reinforcement learning (RL) framework to learn a policy network to optimize QAOA circuits. Our second approach adopts a kernel density estimation (KDE) technique to learn a generative model of optimal QAOA parameters. In both approaches, the training procedure is performed on small-sized problem instances that can be simulated on a classical computer; yet the learned RL policy and the generative model can be used to efficiently solve larger problems. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our proposed RL- and KDE-based approaches reduce the optimality gap by factors up to 30.15 when compared with other commonly used off-the-shelf optimizers.

Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.

Quantifying interfacial parameters of upward and downward annular flow condensation from high-speed visualization

Condensation of R-134a in upward and downward annular flow was experimentally studied in a 5-mm ID transparent test section using a LED-based image processing technique. In addition to the flow orientation, the frequency of the film structures was quantified in terms of the following parameters: total mass flow rate, condensation heat transfer rates (outlet vapor quality) and test section inclination with respect to the horizontal. The image processing technique combined Fourier analysis and the Kernel density estimation technique to calculate the two-dimensional (amplitude–frequency) probability density function of the film flow structure as a function of the independent parameters. Although the tube inclination and the condensation rate showed some influence on the frequency of the interfacial waves, the most significant effect was due to the flow orientation, which resulted in abrupt frequency changes apparently linked to flow regime transitions dictated by shear and gravity forces.

SPATIAL ANALYSIS OF RADON GAS CONCENTRATION DISTRBUTED AT BAGHDAD CITY USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUESD

Spatial analysis (spatial statistics) contain systematic techniques, that study actually geographic data have geographic properties such as topological and geometric, spatial analysis involve difference types of technique. Spatial analysis represented new understanding complex topics in form easy by any method or statistical for study area include mathematics and logic. Using function of point density which represented the Radon gas measurement geographic location, and limitation the density area by finding the radii (bandwidth) Kernel Density Estimation (KDE) techniques variation to reach interest regions and hotspot of point’s neighborhood. The aim of the research, applied the spatial statistical method to acknowledge concentrations of radon gas pollution for environmental and human life conservation, and focuses on the region affected by determination and orientation it in this method or tools for control outbreak to be facilitated, this potential possibility aid to avoid research randomly for radon gas pollution regions, this processing consist by extraction the spatial characteristics distribution that depending on distance measurements, by using geographic information system (GIS) technical, and which exploited Global Positioning system to accurate simultaneously the spectral bands of landsate satellite imagery to referencing the study area, and they have been determining the topology objects such as land cover (water, vegetation and soil) and land use, to connected with points measurement of Radon gas in Baghdad city only soils election, thought the soils were caused pollution based for objects type such as water and air, because, the natural formative of layer soils by decay of radioactive isotope Uranium with erosion the rocks and drift process with dissolved water or flood throughout the ages. Doing field the soil samples measurement by RAD7 Mobil detector for 58 point samples election, which represented using soil type such as vegetation land and land use touched with human life.

A Kernel Density Estimation Based Loss Function and its Application to ASV-Spoofing Detection

Biometric systems are exposed to spoofing attacks which may compromise their security, and voice biometrics, also known as automatic speaker verification (ASV), is no exception. Replay, synthesis and voice conversion attacks cause false acceptances that can be detected by anti-spoofing systems. Recently, deep neural networks (DNNs) which extract embedding vectors have shown superior performance than conventional systems in both ASV and anti-spoofing tasks. In this work, we develop a new concept of loss function for training DNNs which is based on kernel density estimation (KDE) techniques. The proposed loss functions estimate the probability density function (pdf) of every training class in each mini-batch, and compute a log likelihood matrix between the embedding vectors and pdfs of all training classes within the mini-batch in order to obtain the KDE-based loss. To evaluate our proposal for spoofing detection, experiments were carried out on the recent ASVspoof 2019 corpus, including both logical and physical access scenarios. The experimental results show that training a DNN based anti-spoofing system with our proposed loss functions clearly outperforms the performance of the same system being trained with other well-known loss functions. Moreover, the results also show that the proposed loss functions are effective for different types of neural network architectures.

Distributionally Robust Fault Detection by using Kernel Density Estimation

In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.

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.

Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system

The utilization of conventional multivariate control chart in network intrusion detection will deal with two main problems. First, the high false alarm occurs due to the distribution of network traffic data that is not following the theory. Second, the inability of the control chart to detect outliers caused by the masking effect. To overcome these problems, the multivariate control chart based on the fast minimum covariance determinant (MCD) algorithm and kernel density estimation (KDE) is proposed in this paper. The employment of KDE technique is expected to adaptively follow the network traffic data pattern, thereby reducing the occurrence of false alarms. Meanwhile, the usage of Fast-MCD will improve the capabilities of the proposed control chart to quickly and accurately detect the outliers. For the simulated data, the proposed chart shows a better level of accuracy when it is compared to conventional T2 and other robust T2 based on successive difference covariate matrix (SDSM) charts. For the data generated from some distributions, the proposed chart shows its adaptability by producing low false alarm with high detection rate. The proposed chart shows excellent performance to monitor the KDD99 dataset with 98.61% accuracy, NSL-KDD dataset with 91.71% accuracy, and UNSW-NB 15 dataset with 91.02% accuracy. The proposed method has consistent performance when monitoring the small subset of the datasets, which can minimize the computational time by more than 90% without decreasing its level of accuracy and precision. Also, the performance from the proposed chart surpasses the other benchmarks.

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.

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.

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.

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.