Automatic Step Research Articles

Research on Clustering Recognition Algorithm without any Prior Information

Aiming at the problem that the stability of clustering recognition algorithm without any prior information is not enough and the network cluster extraction quality needs to be improved and so on, the original algorithm is improved from the following aspects: The choice of the destination vertex function is added in the random walk; use automatic step number detection method to cancel the original algorithm of random walk steps parameter; add weight of evaluation inter-cluster connection on the original based on cluster extraction conditions; realize the controllable particle size of cluster extraction by introducing the threshold parameter. The test results show that the improved algorithm in terms of anti-jamming in the stability and the clustering quality are higher than the original algorithm.

A Fast and Robust Adaptive Blind Equalization Algorithm in High Speed Satellite Channel

In this paper, a novel combined leakage CMA and direct decision (DD) adaptive algorithm using automatic iteration step adaptive blind equalizer is presented. In contrast to known CMA algorithm, the novel algorithm can approach the performance of mean-square error and fast convergent rate. For adaption of filters, efficient automatic iteration step and DD algorithm is present. The novel algorithm is lower computational complexity withO(N) and N is the filter order. Furthermore, the relation of novel algorithm to CMA algorithm is investigated. Finally, it is shown that proposed combined LCMA-DD algorithm is robust against amplitude and phase offset with fast convergence and mean-square error.

Semi-automatic spike sorting with high-count channel probes

Automatically extracting spiking information from extracellular recordings is a fundamental but still unresolved issue in experimental neuroscience, despite decades of efforts [1]. A fully automatic spike sorting algorithm appears to be out of reach today, mainly because of the large diversity in experimental settings and protocols. Besides, in-vivo recordings are typically highly noisy, making extremely difficult the separation of neuron sources in the spiking activity. Additionally, the development of new silicon probes with a very high number of channels raises new computational problems that require novel approaches [2]. Most existing methods are based on semi-automatic algorithms, with a first, automatic step and a second, manual step requiring the neurophysiologist's interaction. The manual step is typically required because no algorithm yet has the required expertise to check the quality of a spike sorting result. The experimenter is then given a chance to check, validate, and refine the original result. This step can be particularly long and may prevent the neurophysiologist from focusing on the scientific questions of interest underlying his experiments. We are developing a set of tools that aim at making spike sorting sessions as efficient as possible in terms of computer time, human time, and sorting quality. We are currently focusing on improving the manual step by developing a new ergonomic graphical user interface. This interface guides the user through the automatic algorithm's output and asks him to make decisions about ambiguous clusters of spikes. Similar clusters that are likely to stem from the same neuron according to a probability metric are automatically selected. The software also chooses automatically the best feature projection that maximizes the distance between the clusters. The user can then decide whether these clusters should be effectively merged. Several interactive views on the data are available to help the user make the best decisions. Together, these steps allow to improve the resulting spike trains quality. We have chosen to develop this suite of tools in Python, an increasingly used language in the scientific community [3]. Performance is achieved through thin bindings around highly optimized low-level libraries such as Numpy (vectorized computations), HDF5 (efficient random-access input/output), Qt (graphical user interface) and OpenGL (open and portable hardware-accelerated visualization library). Our approach should make possible the analysis of coordinated spiking activity across hundreds of neurons in in-vivo recordings, a crucial step in understanding the neurophysiological bases of behavior.

Global Selection of Stream Surfaces

AbstractStream surfaces are well‐known and widely‐used structures for 3D flow visualization. A single surface can be sufficient to represent important global flow characteristics. Unfortunately, due to the huge space of possible stream surfaces, finding the globally most representative stream surface turns out to be a hard task that is usually performed by time‐consuming manual trial and error exploration using slight modifications of seed geometries. To assist users we propose a new stream surface selection method that acts as an automatic preprocessing step before data analysis. We measure stream surface relevance by a novel surface‐based quality measure that prefers surfaces where the flow is aligned with principal curvature directions. The problem of seed structure selection can then be reduced to the computation of simple minimal paths in a weighted graph spanning the domain. We apply a simulated annealing‐based optimization method to find smooth seed curves of globally near‐optimal stream surfaces. We illustrate the effectiveness of our method on a series of synthetic and real‐world data sets.

Algorithms for Solving Darcian Flow in Structured Porous Media

This paper presents several algorithms that were implemented in DRUtES [1], a new open source project. DRUtES is a finite element solver for coupled nonlinear parabolic problems, namely the Richards equation with the dual porosity approach (modeling the flow of liquids in a porous medium). Mass balance consistency is crucial in any hydrological balance and contaminant transportation evaluations. An incorrect approximation of the mass term greatly depreciates the results that are obtained. An algorithm for automatic time step selection is presented, as the proper time step length is crucial for achieving accuracy of the Euler time integration method. Various problems arise with poor conditioning of the Richards equation: the computational domain is clustered into subregions separated by a wetting front, and the nonlinear constitutive functions cover a high range of values, while a very simple diagonal preconditioning method greatly improves the matrix properties. The results are presented here, together with an analysis.

To Do It or to Let an Automatic Tool Do It?

The aim of the present study is to provide experimental data relevant to the issue of what leads humans to use automatic tools. Two answers can be offered. The first is that humans strive to minimize physical and/or cognitive effort (principle of least effort). The second is that humans tend to keep their perceived control over the environment (principle of more control). These two factors certainly play a role, but the question raised here is to what do people give priority in situations wherein both manual and automatic actions take the same time - minimizing effort or keeping perceived control? To answer that question, we built four experiments in which participants were confronted with a recurring choice between performing a task manually (physical effort) or in a semi-automatic way (cognitive effort) versus using an automatic tool that completes the task for them (no effort). In this latter condition, participants were required to follow the progression of the automatic tool step by step. Our results showed that participants favored the manual or semi-automatic condition over the automatic condition. However, when they were offered the opportunity to perform recreational tasks in parallel, the shift toward manual condition disappeared. The findings give support to the idea that people give priority to keeping control over minimizing effort.

MSPE/UV for field detection of micropollutants in water

This paper proposes a new approach for the on-site detection of micropollutants in water in case of accidental or intentional contamination (characterized by high concentration). The technique is based on the use of an automatic multiple solid phase extraction step (MSPE) followed by direct or indirect UV spectrometry (MSPE/UV) for the detection of organic contaminants in water (such as regulated molecules (pesticides …) as well as emerging pollutants (pharmaceutical products, endocrine disruptors …)). The development of the system supposes firstly the choice of the most appropriate sorbent(s) and secondly conditions for the detection of targeted micropollutants, from several solid phase extraction cartridges and eluting solvents. Two different extraction sorbents were chosen. These sorbents and several eluting solvents allow the first separation of the compounds based on the physico-chemical properties of each substance (pKa, log Kow) and on the specific interactions with the sorbent. Finally, a UV analysis (either at the maximum of absorbance using a calibration curve or from the whole spectrum using a multicomponent (deconvolution) exploitation method) of each fraction allows a determination and a quantification of each compound. The method is rapid (less than 2h), sensitive enough for an accidental/intentional contamination (between 5 and 40μg·L−1 according to the substances) and with a good precision (between 4 and 14%).

Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting

The weather forecasting is considered a rather difficult problem due to many complex features present in these time series. Several techniques have been proposed in the literature to solve this problem. In particular, the dilation-erosion perceptron (DEP), a model whose foundations are based on mathematical morphology and complete lattice theory, has been successfully used for time series forecasting. However, a drawback arises from the gradient estimation of morphological operators in the classical gradient-based learning process of the DEP, since they are not differentiable of usual way. In this sense, this work presents evolutionary learning processes, using a modified genetic algorithm, a particle swarm optimization, a modified differential evolution and a covariance matrix adaptation evolutionary strategy, to design the DEP model for weather forecasting. In addition, into the proposed learning processes we have included an automatic correction step that is geared toward eliminating time phase distortions that occur in some weather phenomena. An experimental analysis is presented using three non-linear forecasting problems from the Brazilian weather, and the obtained results are discussed and compared, according to five well-known performance metrics and an evaluation function, to results found using the DEP model with its classical gradient-based learning process.

Automated methods for accurate determination of the critical velocity of packed bed chromatography

Knowing the critical velocity (ucrit) of a chromatography column is an important part of process development as it allows the optimization of chromatographic flow conditions. The conventional flow step method for determining ucrit is prone to error as it depends heavily on human judgment. In this study, two automated methods for determining ucrit have been developed: the automatic flow step (AFS) method and the automatic pressure step (APS) method. In the AFS method, the column pressure drop is monitored upon application of automated incremental increases in flow velocity, whereas in the APS method the flow velocity is monitored upon application of automated incremental increases in pressure drop. The APS method emerged as the one with the higher levels of accuracy, efficiency and ease of application having the greater potential to assist defining the best operational parameters of a chromatography column.

Computational Scheme of Ordinary Differential Equations Integration on the Basis of Differential Taylor Transformation with Automatic Step and Order Selection

Computational Scheme of Ordinary Differential Equations Integration on the Basis of Differential Taylor Transformation with Automatic Step and Order Selection

A Maximum Power Point Tracker with Automatic Step Size Tuning Scheme for Photovoltaic Systems

The purpose of this paper is to study on a novel maximum power point tracking (MPPT) method for photovoltaic (PV) systems. First, the simulation environment for PV systems is constructed by using PSIM software package. A 516 W PV system established with Kyocera KC40T photovoltaic modules is used as an example to finish the simulation of the proposed MPPT method. When using incremental conductance (INC) MPPT method, it usually should consider the tradeoff between the dynamic response and the steady-state oscillation, whereas the proposed modified incremental conductance method based on extension theory can automatically adjust the step size to track the maximum power point (MPP) of PV array and effectively improve the dynamic response and steady-state performance of the PV systems, simultaneously. Some simulation and experimental results are made to verify that the proposed extension maximum power point tracking method can provide a good dynamic response and steady-state performance for a photovoltaic power generation system.

A Parameterization Technique for the Continuation Power Flow Developed from the Analysis of Power Flow Curves

This paper presents an efficient geometric parameterization technique for the continuation power flow. It was developed from the observation of the geometrical behavior of load flow solutions. The parameterization technique eliminates the singularity of load flow Jacobian matrix and therefore all the consequent problems of ill‐conditioning. This is obtained by adding equations lines passing through the points in the plane determined by the loading factor and the total real power losses that is rewritten as a function of the real power generated by the slack bus. An automatic step size control is also provided, which is used when it is necessary. Thus, the resulting method enables the complete tracing of P‐V curves and the computation of maximum loading point of any electric power systems. Intending to reduce the CPU time, the effectiveness caused by updating the Jacobian matrix is investigated only when the system undergoes a significant change. Moreover, the tangent and trivial predictors are compared with each other. The robustness and simplicity as well as the simple interpretation of the proposed technique are the highlights of this method. The results obtained for the IEEE 300‐bus system and for real large systems show the effectiveness of the proposed method.

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting

Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature.

Conceptual Weighted Feature Extraction and Support Vector Model: A Good Combination for Text Categorization

Abstract While weighted features are known in information retrieval (IR) systems to be used for increasing recall during the document selection step, conceptual methods helped for finding good features. Starting from the features of a sample of Arabic news belonging to k different financial categories, and using the support vector model (SVM), k(k-1) classifiers are generated using one-against-one classification. A new document is submitted to k(k-1) different classifiers then by using the voting heuristic, is assigned to the most selected category. Categorization results obtained for two different methods for feature extraction: one based on the optimal concepts and the other based on isolated labels, proved that isolated labels generate better feature, because of the specificity of the selected features. Therefore, we can say that the quality of the feature, added to weighting methods, using SVM is an important factor for a more accurate classification. The proposed method based on isolated labels gives a good classification rate of Arabic news greater than 80% in the financial domain for five categories. Generalized to English Texts and to more categories, it becomes a good preprocessing filtering preceding automatic annotation step, and therefore helps for more accurate event structuring. Here attached a figure showing the different steps of the new categorization method.

Volcanic ash cloud detection from space: a comparison between the RSTASH technique and the water vapour corrected BTD procedure

Volcanic eruptions can inject large amounts (Tg) of gas and particles into the troposphere and, sometimes, into the stratosphere. Besides the main gases (H2O, CO2, SO2 and HCl), volcanic clouds contain a mix of silicate ash particles in the size range from 0.1 μm to 1 mm or larger. The interest in volcanic ash detection is high, particularly because it represents a serious hazard for air traffic. Particles with dimensions of several millimetres can damage the aircraft structure (windows, wings, ailerons), while particles less than 10 μm may be extremely dangerous for the jet engines and are undetectable by the pilots during night or in low visibility conditions. Furthermore, ash detection represents a critical step towards quantitative retrievals of plume parameters. In this paper two different satellite techniques for volcanic cloud detection and tracking are compared, namely a water vapour corrected version of the brightness temperature difference (BTD-WVC) procedure and an implementation of the robust satellite technique, specifically configured for volcanic ash (RSTASH). The BTD method identifies volcanic ash clouds on the basis of the brightness temperature difference measured in two infrared spectral bands at around 11 and 12 μm. To account for the atmospheric water vapour differential absorption in the 11–12 μm spectral range, which tends to reduce (and in some cases completely mask) the BTD signal, a water vapour correction procedure has been developed (BTD-WVC), based on measured or synthetic atmospheric profiles. RSTASH instead, is based on the analysis of a time series of satellite records, aimed at identifying signal anomalies through an automatic unsupervised change detection step. To assess the performance of the BTD-WVC and RSTASH methods in detecting volcanic ash clouds, some eruptive events of Mt Etna, observed by the Advanced Very High Resolution Radiometer (AVHRR) sensor, have been analysed. The obtained results show a good agreement between the BTD-WVC and RSTASH techniques for all the considered images, in terms of pixels detected as ‘ash affected’ (i.e. the ash cloud area). In particular, compared to the traditional BTD procedure, the BTD-WVC and RSTASH techniques significantly improve volcanic ash cloud detection, both in daytime and night-time data, especially in the case of low ash loading.

Quantitative measurement of the homogeneity and contrast of step edges in the estimation of the point spread function of a satellite image

It is important to measure the spatial performance of an in-flight satellite sensor to determine if any spatial resolution degradations occur over time; then some compensation methods can be adopted to improve the image quality. In satellite on-orbit measurements of spatial resolution, the performance of the sensors can be objectively evaluated through the point spread function (PSF) or modulation transfer function (MTF). The well-known image-based PSF derivation method of sampling along a slanted linear step structure from the degraded image is widely used in the satellite image restoration area. Due to the non-uniformity of the linear ground objects, generally, the operators have to pick out good profiles from all the profiles along the edge in order to increase the estimation accuracy. However, only some qualitative criteria have been proposed in the literature to instruct researchers how to pick out good profiles. In this article, we propose a method to quantitatively measure the homogeneity and contrast of the candidate linear structures in order to extract the optimal profiles. This method first automatically detects and locates the slanted linear step edge object in the image. Then it measures the homogeneity and the contrast at each location along the edge by using two adjacent square regions on both sides of the linear step structure. The locations that have high measurement values are regarded as the optimal locations. Finally, the optimal profiles can be extracted at these edge locations to derive an accurate PSF estimation by differentiation. This method makes an automatic optimal step edge detection method for PSF derivation to be practical. Experiment results on simulated and real images are provided. This article shows that using only the ‘optimal’ profiles can play a real and important role and can achieve a good PSF estimation result.

Determination of nickel in cigarettes smoke by molecular fluorescence

Smoking represents the main avoidable cause of disease and death in the world. Cigarette smoke contains over 4700 chemical compounds including 60 known carcinogens. The inhalation of tobacco smoke is one of the most significant sources of nickel exposure for occupationally unexposed population. The aim of this work was to evaluate nickel contents present in cigarette smoke by using an automatic sample preparation step and solid surface fluorescence. A total of 250 cigarettes (brands available in Argentine) were smoked in a smoking device applying vacuum and forcing the mainstream smoke to pass through a filter holder containing a Nylon membrane treated with eosin dye (eo). Nickel present in smoke cigarette was selectively retained by eo as an association compound. Chemically enriched nickel on nylon membranes was subsequent quantified by spectrofluorimetry (λem=545nm, λexc=515nm), reaching quantitative recovery with a detection limit of 1.56ngL−1 and quantification limit of 5.52ngL−1. The calibration sensitivity using zeroth order calibration was 1 1012ngL−1 (r2=0.9992) for the methodology with a linear range of 5.52 to 5.17 104ngL−1 Ni(II). Standard addition method was satisfactorily applied to nickel determination in mainstream smoke cigarettes of twenty brands. Results can be good correlated with urinary nickel contents coming from fifty subjects with different levels of addiction.

StochKit2: software for discrete stochastic simulation of biochemical systems with events

StochKit2 is the first major upgrade of the popular StochKit stochastic simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie's stochastic simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/. petzold@engineering.ucsb.edu.

Walk to me when I smile, step back when I’m angry: emotional faces modulate whole-body approach–avoidance behaviors

Facial expressions are potent social cues that can induce behavioral dispositions, such as approach–avoidance tendencies. We studied these tendencies by asking participants to make whole-body forward (approach) or backward (avoidance) steps on a force plate in response to the valence of social cues (happy or angry faces) under affect-congruent and incongruent mappings. Posturographic parameters of the steps related to automatic stimulus evaluation, step initiation (reaction time), and step execution were determined and analyzed as a function of stimulus valence and stimulus–response mapping. The main result was that participants needed more time to initiate a forward step towards an angry face than towards a smiling face (which is evidence of a congruency effect), but with backward steps, this difference failed to reach significance. We also found a reduction in spontaneous body sway prior to the step with the incongruent mapping. The results provide a crucial empirical link between theories of socially induced action tendencies and theories of postural control and suggest a motoric basis for socially guided motivated behavior.

Organization of voluntary stepping in response to emotion-inducing pictures

The present experiment was conducted to examine the expectation that emotion stimuli influence the initiation and execution of voluntary stepping, a highly coordinated activity involving a sequence of medio-lateral and anterio-posterior weight shifts. Thirty participants made forward (approach) or backward (avoidance) steps on a forceplate in response to the valence of visual stimuli. Posturographic parameters of the steps, related to automatic stimulus evaluation, step initiation and step execution, were determined and analyzed as a function of stimulus valence and stimulus-response mapping. The results revealed marked effects of emotion on the step parameters of interest; unpleasant images caused an initial “freezing” response, and a tendency to move away from the stimuli. Pleasant stimuli, in contrast, were not found to induce approach tendencies. The results demonstrated that affect, especially negative emotions, and whole-body movements such as voluntary stepping are coupled.