Dimension Of Input Space Research Articles

An Integrated Gaussian Process Modeling Framework for Residential Load Prediction

While adding new capabilities, the distributed energy resource (DER) proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For robust and efficient operational planning purposes, we propose an integrated Gaussian process (IGP) modeling framework for reliable hourly load prediction. The proposed IGP modeling framework has the following unique features: 1) the IGP utilizes not only the data streams generated by the target customer, but also those generated by relevant customers in the power system; an effective input space dimension reduction method is proposed to significantly improve the computational efficiency, while maintaining the high predictive accuracy of the IGP; and 2) an adaptive data communication rate controlling scheme is proposed to further enhance the predictive performance of the IGP by optimally and dynamically adjusting the data communication rate used for each customer under the total data communication bandwidth constraint often imposed. Taking into account the highly uncertain load and generation behaviors of DERs, the proposed IGP framework is tested on various standard IEEE test cases with load and renewable generation data collected from real-world power systems with DERs. The superiority and efficacy of the IGP are verified by our simulation results.

Deep learning: A guide for practitioners in the physical sciences

Machine learning is finding increasingly broad applications in the physical sciences. This most often involves building a model relationship between a dependent, measurable output, and an associated set of controllable, but complicated, independent inputs. We present a tutorial on current techniques in machine learning—a jumping-off point for interested researchers to advance their work. We focus on deep neural networks with an emphasis on demystifying deep learning. We begin with background ideas in machine learning and some example applications from current research in plasma physics. We discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, and then advancing to more sophisticated deep learning methods. We also address unsupervised learning and techniques for reducing the dimensionality of input spaces. Along the way, we describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We describe classes of tasks—predicting scalars, handling images, and fitting time-series—and prepare the reader to choose an appropriate technique. We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help.

Fuzzy self organizing maps-based DDoS mitigation mechanism for software defined networking in cloud computing

The characteristic features of cloud computing deployment make it highly vulnerable to distributed denial of service (DDoS) attacks. The recent advancement in software-defined networking (SDN) enhances the possibilities for defeating DDoS attacks in cloud computing environments. This option to improve the probability of defeating DDoS attacks is made feasible through the striking features of SDN that include their capability for software-oriented traffic investigation, network global dimension, dynamically updating forwarding rules and centralized point of control. This paper presents a Fuzzy self organizing maps-based DDOS mitigation (FSOMDM) technique that is ideally and suitably designed for improving the SDN capabilities of cloud computing. FSOMDM is the enhanced neural network model that effectively replaces the neurons of the traditional Kohonen neural network model through updating fuzzy rules. The property of software-oriented traffic investigation is utilized in this process and the fuzzy rule is used for exploring the dimension of input space from which a single valued output is derived for enabling the mitigation of DDoS. In addition, FSOMDM incorporates an attack-response process that possesses the significance of dropping attack flows through its enforcement in the control plane of SDN. The performance investigation of FSOMDM confirms its significance by facilitating nearly 94% of classifier accuracy evaluated in terms of true positive rate (TPR).

Stabilization and Equilibrium Control for Electrically Cart–Seesaw Systems by Neuro-fuzzy Approach

Compared to completely control systems, super-articulated mechanical systems (SAMS) are controlled under-actuated systems where the dimensions of the control input space are less than the magnitudes of the output. Hence, the major aim of this study is developing an innovative cart–seesaw system which is manipulated by electrical servo actuators and also improving balancing methodology for such apparatus. Controlling the mobile cart on the seesaw is exceptionally hard because it is an under-actuated machinery. With the purpose of the stabilization, the famous PID control scheme will be based on the genetic algorithm (GA) to pursue the main optimum parameters, and then enhancement of a fuzzy logic approach to stabilize the nonlinear model. The final controller will be completed by treating an intelligent fuzzy logic controller (FLC) based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning approach to speed up the effectiveness of controller construction. The knowledge base of fuzzy system was subsequently built on PID performance-related information. Experimentation shows that applying the suggested new cart–seesaw system (CSS) shows better functioning than the previous pneumatic cart–seesaw (PCS) system in tracking and balancing execution. Furthermore, the balancing approach presented in this paper meaningfully indicates the valuableness of the suggested intelligent controller in suppression oscillation of the seesaw. The investigation of the proposed system will be useful for control course apparatus and also worthy for the entertainment device.

Robust clustering using a kNN mode seeking ensemble

In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a clustering ensemble consisting of repeated and bootstrapped runs of the recent kNN mode seeking algorithm, an algorithm which is faster than ordinary mean shift and more suited for high dimensional data. This creates a robust mode seeking clustering algorithm with respect to the choice of parameters and high dimensional input spaces, while at the same inheriting all other strengths of mode seeking in general. We demonstrate promising results on a number of synthetic and real data sets.

Latent‐lSVM classification of very high‐dimensional and large‐scale multi‐class datasets

SummaryWe propose a new parallel learning algorithm of latent local support vector machines (SVM), called latent‐lSVM for effectively classifying very high‐dimensional and large‐scale multi‐class datasets. The common framework of texts/images classification tasks using the Bag‐Of‐(visual)‐Words model for the data representation leads to hard classification problem with thousands of dimensions and hundreds of classes. Our latent‐lSVM algorithm performs these complex tasks into two main steps. The first one is to use latent Dirichlet allocation for assigning the datapoint (text/image) to some topics (clusters) with the corresponding probabilities. This aims at reducing the number of classes and the number of datapoints in the cluster compared to the full dataset, followed by the second one: to learn in a parallel way nonlinear SVM models to classify data clusters locally. The numerical test results on nine real datasets show that the latent‐lSVM algorithm achieves very high accuracy compared to state‐of‐the‐art algorithms. An example of its effectiveness is given with an accuracy of 70.14% obtained in the classification of Book dataset having 100 000 individuals in 89 821 dimensional input space and 661 classes in 11.2 minutes using a PC Intel(R) Core i7‐4790 CPU, 3.6 GHz, 4 cores.

A SOM prototype-based cluster analysis methodology

An original computational approach for cluster analysis is proposed.The method consists of two phases, which are based on Self-Organizing Map.Topology-preserving and connectivity functions are used in the clustering process.The method is proved using three benchmark datasets and a real biological dataset.Automation in parameterization results in a user-friendly methodology. Data clustering is aimed at finding groups of data that share common hidden properties. These kinds of techniques are especially critical at early stages of data analysis where no information about the dataset is available. One of the mayor shortcomings of the clustering algorithms is the difficulty for non-experts users to configure them and, in some cases, interpret the results. In this work a computational approach with a two-layer structure based on Self-Organizing Map (SOM) is presented for cluster analysis. In the first level, a quantization of the data samples using topology-preserving metrics to automatically determine the number of units in the SOM is proposed. In the second level the obtained SOM prototypes are clustered by means of a connectivity analysis to explore the quality of the partitioning with different number of clusters. The most important benefit of this two-layer procedure is that computational load decreases considerably in comparison with data based clustering methods, making it possible to cluster large data sets and to consider several different clustering alternatives in a limited time. This methodology produces a two-dimensional map representation of the, usually, high dimensional input space, along with quantitative information on viable clustering alternatives, which facilitates the exploration of the possible partitions in a dataset. The efficiency and interpretation of the methodology is illustrated by its application to artificial, benchmark and real complex biological datasets. The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution. The proposed algorithm tackles the intrinsic limitations of SOM and the parameter settings associated with the clustering methodology, without requiring the number of clusters or the SOM architecture as a prerequisite, among others. This way, it makes possible its application even by researchers with a limited expertise in machine learning.

Predictive performances of implicitly and explicitly robust classifiers on high dimensional data

The goal of this paper is to demonstrate via extensive simulation that implicit robustness can substantially outperform explicit robust inthe pattern recognition of contaminated high dimension low sample size data.Our work specially demonstrates via extensive computational simulations and applications to real life data, that random subspace ensemble learning machines, although not explicitly structurally designed as a robustness-inducing supervised learning paradigms, outperforms the structurally robustness-seekingclassiers on high dimension low sample size datasets. Random forest (RF),which is arguably the most commonly used random subspace ensemble learning method, is compared to various robust extensions/adaptations of the discriminant analysis classier, and our work reveals that RF, although not inherently designed to be robust to outliers, substantially outperforms the existing techniques specically designed to achieve robustness. Specically, by exploring different scenarios of the sample size n and the input space dimensionality palong with the corresponding capacity κ = n/p with κ < 1, we demonstratethrough extensive simulations that regardless of the contamination rate ϵ, RF predictively outperforms the explicitly robustness-inducing classication techniques when the intrinsic dimensionality of the data is large

Spatial Modelling of Extreme Wind Speed Distributions in Switzerland

The understanding of wind phenomena is of primary importance, especially regarding the renewable energy. One traditional way of wind modelling concerns the use of parametric probability distributions. This work presents a method in two steps to model this phenomena. The first of which studies and compares different extreme distributions by modelling data collected from the Swiss meteorological service. This comparison should serve as a basis for the second step, which applies spatial modelling of distribution parameters throughout the country. The modelling is performed in a high dimensional input space with the help of extreme learning machine. The knowledge of probability distributions allows us to give a comprehensive information about a wind speed distribution.

A Novel Method Based on Empirical Mode Decomposition for P300-Based Detection of Deception

Conventional polygraphy has several alternatives and one of them is P300-based guilty knowledge test. The purpose of this paper is to apply a new method called empirical mode decomposition (EMD) to extract features from electroencephalogram (EEG) signal. EMD is an appropriate tool to deal with the nonlinear and nonstationary nature of EEG. In the previous studies on the same data set, some morphological, frequency, and wavelet features were extracted only from Pz channel, and used for the detection of guilty and innocent subjects. In this paper, an EMD-based feature extraction was done on EEG recorded signal. Features were extracted from all three recorded channels (Pz, Cz, and Fz) for synergistic incorporation of channel information. Finally, a genetic algorithm was utilized as a tool for efficient feature selection and overcoming the challenge of input space dimension increase. The classification accuracy of guilty and innocent subjects was 92.73%, which was better than other previously used methods.

Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils

Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.

Real-time capable first principle based modelling of tokamak turbulent transport

A real-time capable core turbulence tokamak transport model is developed. This model is constructed from the regularized nonlinear regression of quasilinear gyrokinetic transport code output. The regression is performed with a multilayer perceptron neural network. The transport code input for the neural network training set consists of five dimensions, and is limited to adiabatic electrons. The neural network model successfully reproduces transport fluxes predicted by the original quasilinear model, while gaining five orders of magnitude in computation time. The model is implemented in a real-time capable tokamak simulator, and simulates a 300 s ITER discharge in 10 s. This proof-of-principle for regression based transport models anticipates a significant widening of input space dimensionality and physics realism for future training sets. This aims to provide unprecedented computational speed coupled with first-principle based physics for real-time control and integrated modelling applications.

Gaussian mixture regression and local linear network model for data‐driven estimation of air mass

The conventional approaches for modelling of charge cycle in combustion engines are based on first principles. In this approaches, it is necessary to estimate different parameters in engine, which is very difficult to implement in real-time. In addition, it requires a high parameterisation effort because of a variety of engine characteristics. The model is further used in engine control unit to improve the efficiency of the combustion. Moreover, it can be used for estimation of some physical variables in engine to increase the performance of the controller. It also avoids the installation of extra sensors and reduces the costs of production. In this study, two different data-driven modelling methods for estimation of air mass are investigated. They also offer a flexible modelling approach considering various options for equipment as well as sensors and actuators in the engines. Both data-driven methods, the Gaussian mixture regression (GMR) and the local linear model tree (LOLIMOT) algorithm, allow a flexible modelling with a high input space dimensionality, with sparsity of data, a good interpretability and also offer possibilities of adaptation and local optimisation. While the GMR uses a statistical approaches to train the model, in the LOLIMOT an incremental, heuristic approach is used. Both methods are applied to the data obtained from a spark-ignition engine and the results are compared and discussed.

Discernible visualization of high dimensional data using label information

Visualization methods could significantly improve the outcome of automated knowledge discovery systems by involving human judgment. Star coordinate is a visualization technique that maps k-dimensional data onto a circle using a set of axes sharing the same origin at the center of the circle. It provides the users with the ability to adjust this mapping, through scaling and rotating of the axes, until no mapped point-clouds (clusters) overlap one another. In this state, similar groups of data are easily detectable. However an effective adjustment could be a difficult or even an impossible task for the user in high dimensions. This is specially the case when the input space dimension is about 50 or more.In this paper, we propose a novel method toward automatic axes adjustment for high dimensional data in Star Coordinate visualization method. This method finds the best two-dimensional view point that minimizes intra-cluster distances while keeping the inter-cluster distances as large as possible by using label information. We call this view point a discernible visualization, where clusters are easily detectable by human eye. The label information could be provided by the user or could be the result of performing a conventional clustering method over the input data. The proposed approach optimizes the Star Coordinate representation by formulating the problem as a maximization of a Fisher discriminant. Therefore the problem has a unique global solution and polynomial time complexity. We also prove that manipulating the scaling factor alone is effective enough for creating any given visualization mapping. Moreover it is showed that k-dimensional data visualization can be modeled as an eigenvalue problem. Using this approach, an optimal axes adjustment in the Star Coordinate method for high dimensional data can be achieved without any user intervention. The experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and performance.

A fault tolerant single-chip intelligent agent with feature extraction capability

Autonomy and adaptability are key features of intelligent agents. Many applications of intelligent agents, such as the control of ambient intelligence environments and autonomous intelligent robotic systems, require the processing of information coming in from many available sensors to produce adequate output responses in changing scenarios. Autonomy, in these cases, applies not only to the ability of the agent to produce correct outputs without human guidance, but also to its ubiquity and/or portability, low-power consumption and integrability. In this sense, an embedded electronic system implementation paradigm can be applied to the design of autonomous intelligent agents in order to satisfy the above mentioned characteristics. However, processing complex computational intelligence algorithms with tight delay constraints in resource-constrained and low power embedded systems is a challenging engineering problem. In this paper a single-chip intelligent agent based on a computationally efficient neuro-fuzzy information processing core is described. The system has been endowed with an information preprocessing module based on Principal Component Analysis (PCA) that permits a substantial reduction of the input space dimensionality with little loss of modeling capability. Moreover, the PCA module has been tested as a means to achieve deep adaptability in changing environment dynamics and to endow the agent with fault tolerance in the presence of sensor failures. For data driven trials and research, a data set obtained from an experimental intelligent inhabited environment has been used as a benchmark system.

Water quality assessment in the Harbin reach of the Songhuajiang River (China) based on a fuzzy rough set and an attribute recognition theoretical model.

A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.

Global optimization ensemble model for classification methods.

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

Application of Extreme Learning Machine in Transient Stability Assessment of Power Systems

This paper presents a new method for transient stability assessment (TSA) of power systems using kernel fuzzy rough sets and extreme learning machine (ELM). Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features were firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets were used to reduce the dimension of input space, and ELM was employed to build a TSA model. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus test system.

A time-sequence-based fuzzy support vector machine adaptive filter for tremor cancelling for microsurgery

Hand tremors may cause some blemishes in precision and stability of a minimally invasive surgery (MIS). To track the tremor signals accurately, there are two main problems left to be settled. First, it is not practical to collect the sample data of tremor in large scale in practical applications. To deal with the hand tremors, a learning method based on small samples sizes and high dimensional input space is needed. Second, the hand tremors have time-varying characteristics. This fact is neglected by traditional learning methods, which could lead to imprecision and instability of a MIS. In this work, a time-sequence-based fuzzy support vector machine adaptive filter (TSF-SVMAF) for tremor cancelling is proposed. The proposed method is based on support vector machine and time series. It is suitable for solving the problem that the inputs are time-varying and the samples are small-scale. To cancel the time-varying hand tremors, different learning-weight-functions are designed for tremor signals with different frequencies. From the simulation results, compared with the existing methods such as back propagation (BP), weighted-frequency Fourier combiner (WFLC) and bandlimited multiple Fourier linear combiner (BMFLC), the proposed method has better performance when learning the time-varying hand tremors with small sample sizes.

Testing over-representation of observations in subsets of a DEA technology

This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space. Potential applications include tests for whether benchmark units dominate more (or less) observations than expected.