User-defined Value Research Articles

Comparing phase sensitive detection and Fourier analysis of modulation excitation spectroscopy data exemplified by Ambient Pressure X-ray Photoelectron Spectroscopy

Dynamic processes in catalysis are gaining increased attention and could very well be one of the next frontiers in surface science. One way to study such processes is to induce chemical changes on the surface for example by periodically adjusting the (electro)chemical potential in situ and identify the resulting spectral changes. Often this is referred to as Modulation Excitation Spectroscopy (MES). Using Ambient Pressure Photoelectron Spectroscopy data, we here discuss and compare the analysis of MES data using both Phase Sensitive Detection (PSD) and Fourier analysis. We discuss that PSD determines the component magnitude at a user-defined phase value while Fourier analysis provides the maximum oscillation amplitude and respective phase value of oscillating spectral features. We discuss advantages and disadvantages of the different analysis schemes and explore how the full time-evolution can be obtained.

Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

Early attack detection is essential to ensure the security of complex networks, especially those in critical infrastructures. This is particularly crucial in networks with multi-stage attacks, where multiple nodes are connected to external sources, through which attacks could enter and quickly spread to other network elements. Bayesian attack graphs (BAGs) are powerful models for security risk assessment and mitigation in complex networks, which provide the probabilistic model of attackers’ behavior and attack progression in the network. Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring, which is unrealistic given the ever-growing complexity of threats. This paper derives the optimal minimum mean square error (MMSE) attack detection and monitoring policy for the most general form of BAGs. By exploiting the structure of BAGs and their partial and imperfect monitoring capacity, the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering. An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value. Exact and efficient matrix-form computations of the proposed policies are provided, and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

Estimation of biosurfactant production parameters and yields without conducting additional experiments on a larger production scale

In this study, a Plackett-Burman design was applied to investigate critical factors for surface tension. After adding a new factor called “production scale”, a central composite design (CCD) was constructed to examine nonlinear relations among factors and surface tension. An artificial neural network (ANN) was trained using data from CCD experiments. The ANN with the best structure of 5–6-1 was then tested with different unseen data sets. The predictions from ANN were within the 95% confidence interval (CI), even for a larger production scale, determined by using the replicates. A genetic algorithm (GA) was developed to check how the values of the factors vary if the production scale was set to a user-defined value. Based on the validation experiments, it was observed that the 95% confidence interval of surface tension was 36.83 ± 1.00 mN m−1 while pH 8, temperature 35 °C, incubation time 12 h, and amount of inoculum 2.30%, respectively, for the production scale of 600 mL. The proposed methodological approach with the integration of ANN and GA is considered to make a serious economic contribution as it allows predicting a proper setup for larger production scales without conducting additional experiments.

Graphical Image Region Extraction with K-Means Clustering and Watershed.

With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

An adaptive isogeometric phase-field method for brittle fracture in rock-like materials

In recent years, the phase-field method (PFM) has gained considerable attention to model fracture process. This is because, in a single framework, the method allows to model crack nucleation, growth, coalescence and branching. In addition, existing FE codes can be used with minimal modifications. However, one of the major limitations of the PFM is that, it requires extremely fine mesh to accurately capture the fracture process. An adaptive phase-field method within the framework of isogeometric analysis is presented in this work to model tensile and compressive-shear crack propagation in rock-like materials. Locally refined NURBS are adopted for describing both the geometry and the field variables. The local refinement is performed by structured mesh refinement strategy based on an error indicator that depends on the user-defined threshold value for the phase-field variable. Two phase-field models are adopted for simulating tensile and compressive-shear fractures in rock-like materials. For numerical implementation, a hybrid formulation combined with a staggered solution scheme is adopted. Several two-dimensional benchmark problems are investigated to demonstrated the robustness of the proposed framework. From the numerical study, it can be found that the developed adaptive framework achieves results comparable to a uniformly refined mesh with higher degrees of freedom.

A Flow Sensor-Based Suction-Index Control Strategy for Rotary Left Ventricular Assist Devices.

Rotary left ventricular assist devices (LVAD) have emerged as a long-term treatment option for patients with advanced heart failure. LVADs need to maintain sufficient physiological perfusion while avoiding left ventricular myocardial damage due to suction at the LVAD inlet. To achieve these objectives, a control algorithm that utilizes a calculated suction index from measured pump flow (SIMPF) is proposed. This algorithm maintained a reference, user-defined SIMPF value, and was evaluated using an in silico model of the human circulatory system coupled to an axial or mixed flow LVAD with 5–10% uniformly distributed measurement noise added to flow sensors. Efficacy of the SIMPF algorithm was compared to a constant pump speed control strategy currently used clinically, and control algorithms proposed in the literature including differential pump speed control, left ventricular end-diastolic pressure control, mean aortic pressure control, and differential pressure control during (1) rest and exercise states; (2) rapid, eight-fold augmentation of pulmonary vascular resistance for (1); and (3) rapid change in physiologic states between rest and exercise. Maintaining SIMPF simultaneously provided sufficient physiological perfusion and avoided ventricular suction. Performance of the SIMPF algorithm was superior to the compared control strategies for both types of LVAD, demonstrating pump independence of the SIMPF algorithm.

Assessing feature selection method performance with class imbalance data

Identifying the most informative features is a crucial step in feature selection. This paper focuses primarily on wrapper feature selection methods designed to detect important features with F1-score as the target metric. As an initial step, most wrapper methods order features according to importance. However, in most cases, the importance is defined according to the classification method used and varies with the characteristics of the data set. Using synthetically simulated data, we examine four existing feature ordering techniques to find the most desirable and the most effective ordering mechanism to identify informative features. Using the results, an improved method is suggested to extract the most informative feature subset from the data set. The method uses the sum of absolute values of the first k principal component loadings to order the features where k is a user-defined application-specific value. It also applies a sequential feature selection method to extract the best subset of features. We further compare the performance of the proposed feature selection method with results from the existing Recursive Feature Elimination (RFE) by simulating data for several practical scenarios with a different number of informative features and different imbalance rates. We also validate the method using a real-world application on several classification methods. The results based on the accuracy measures indicate that the proposed approach performs better than the existing feature selection methods.

A Framework for Self-Enforced Interaction Between Connected Vehicles: Intersection Negotiation

This paper proposes an algorithm for resolution of traffic conflicts occurring on an intersection, intended for Connected Autonomous Vehicle (CAV). The algorithm is based on the trade-off between energy consumption and user-defined value of time. The consequent cooperation opportunities originating from agent heterogeneity are captured by a game-theoretic cooperative-competitive solution approach to develop a computationally feasible, self-enforced, cooperative intersection de-conflicting algorithm. It is intended as a component of a robust framework for strategic control of the vehicle’s powertrain. Monte Carlo simulation is used to showcase the decision-making algorithm’s behaviour, to estimate its efficiency as a function of traffic heterogeneity. The results confirm that the proposed algorithm may offer threefold improvement in energy and time efficiency in relation to a First Come First Served scheme.

Deterministic and reliability-based design of necessary support pressures for tunnel faces

This paper provides methods for the deterministic and reliability-based design of the support pressures necessary to prevent tunnel face collapse. The deterministic method is developed by extending the use of the unique load multiplier, which is embedded within OptumG2/G3 with the intention of determining the maximum load that can be supported by a system. Both two-dimensional and three-dimensional examples are presented to illustrate the applications. The obtained solutions are validated according to those derived from the existing methods. The reliability-based method is developed by incorporating the Response Surface Method and the advanced first-order second-moment reliability method into the bisection algorithm, which continuously updates the support pressure within previously determined brackets until the difference between the computed reliability index and the user-defined value is less than a specified tolerance. Two-dimensional reliability-based support pressure is compared and validated via Monte Carlo simulations, whereas the three-dimensional solution is compared with the relationship between the support pressure and the resulting reliability index provided in the existing literature. Finally, a parametric study is carried out to investigate the influences of factors on the required support pressure.

Static and modal analysis of diaphragm spring used in micro depth sensing indenting Machine

Abstract Indentation is perhaps the most commonly applied means of testing the mechanical properties of materials. In a traditional indentation test (macro or micro indentation), a hard tip whose mechanical properties are known (frequently made of a very hard material like diamond) is pressed into a sample whose properties are unknown. The load placed on the indenter tip is increased as the tip penetrates further into the specimen and soon reaches a user-defined value. This paper presents the analysis and testing of diaphragm spring used in the Micro Depth Sensing Indenting Machine developed at Indian Institute of Science, Bangalore. Deflection of the spring are found out for various loads using ANSYS and results are validated through physical testing. Modal analysis of spring is carried out to find the possibility of resonance.

Feasibility of Incorporating Test-Retest Reliability and Model Diversity in Identification of Key Neuromuscular Pathways During Head Position Tracking.

To study the complex neuromuscular control pathways in human movement, biomechanical parametric models and system identification methods are employed. Although test-retest reliability is widely used to validate the outcomes of motor control tasks, it was not incorporated in system identification methods. This study investigates the feasibility of incorporating test-retest reliability in our previously published method of selecting sensitive parameters. We consider the selected parameters via this novel approach to be the key neuromuscular parameters, because they meet three criteria: reduced variability, improved goodness of fit, and excellent reliability. These criteria ensure that the parameter variability is below a user-defined value, the number of these parameters is maximized to enhance goodness of fit, and their test-retest reliability is above a user-defined value. We measured variability, the goodness of fit, and reliability using Fisher information matrix, variance accounted for, and intraclass correlation, respectively. We also incorporated model diversity as a fourth optional criterion to narrow down the solution space of key parameters. We applied this approach to the head position tracking tasks in axial rotation and flexion/extension. A total of forty healthy subjects performed the tasks during two visits. With variability and reliability measures ≤0.35 and ≥0.75, respectively, we selected three key parameters out of twelve with the goodness of fit >69%. The key parameters were associated with at least two neuromuscular pathways out of four modeled pathways (visual, proprioceptive, vestibular, and intrinsic), which is a measure of model diversity. Therefore, it is feasible to incorporate reliability and diversity in system identification of key neuromuscular pathways in our application.

Development and Evaluation of a Degree of Hybridisation Identification Strategy for a Fuel Cell Supercapacitor Hybrid Bus

Fuel cells (FC) are a clean energy source that are capable of powering a vehicle’s electrical energy requirements whilst providing zero operating emissions. In this study, a full-scaled computer model FC/supercapacitor (SC) hybrid has been developed to investigate the performance of the hybrid propulsion system under real-world performance conditions. A control strategy focused on maintaining a constant FC output at a user-defined value has been developed and applied to the FC/SC hybrid model. Driving cycles collected from a practical double-decker bus have been utilised to evaluate the developed model. It has been demonstrated that the proposed control strategy is capable of maintaining a constant and stable FC output while meeting a real world dynamic load. Based on the obtained results, a general strategy to identify the degree of hybridisation between the FC and the SC in a FC hybrid system has been developed and demonstrated.

Constrained Extremum Seeking: a Modified-Barrier Function Approach

In this paper, we address the problem of steering the input of a convex function to a value that minimizes the function under a convex constraint. We consider the case where the constraint cannot be violated of more than a user-defined value during the whole transient phase. The mathematical expression of both the cost function and the constraint are assumed to be unknown. The only information available are the on-line values of the cost and the constraint. To tackle this problem, an optimization law, based on a modified-barrier function, and involving the gradient of both the cost function and the constraint, is firstly designed. The Lie bracket formalism is then exploited to approximate this law, by combining time-periodic signals with the on-line measurements of both the cost and the constraint. The stability property of the resulting constrained extremum seeking system is proved, and its effectiveness is shown in simulation.

Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure.

Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.

Fault-tolerant tile mining

Interesting itemset mining is a fundamental research problem in knowledge management and machine learning. It is intended to identify interesting relations between variables in a database using some measures of interestingness and has a number of applications, including market basket analysis, web usage mining, intrusion detection, and many others. This paper proposes a new interestingness measure, the fault-tolerant tile. That is based on two observations: (1) the length of an itemset can be as important as its frequency; (2) knowledge discovery from real-world datasets calls for fault-tolerant data mining (e.g. extracting fault-tolerant association rules, analyzing noisy datasets). Given a user-defined fault tolerance value, we are interested in finding the maximum/top-k fault-tolerant tiles. Due to the exponential search space of candidate itemsets, both problems are NP-hard. While using some monotonic property to prune search space is a common strategy for interesting itemset mining, no monotonic property is available for this problem. To tackle the challenge, we utilize the branch-and-bound search strategy to analyze the characteristics of candidate itemsets at each searching branch and estimating their bounds. Our experimental results show that our algorithms can effectively analyze real datasets and retrieve meaningful results.

Kernel-Based Visual Hazard Comparison (kbVHC): a Simulation-Free Diagnostic for Parametric Repeated Time-to-Event Models

Repeated time-to-event (RTTE) models are the preferred method to characterize the repeated occurrence of clinical events. Commonly used diagnostics for parametric RTTE models require representative simulations, which may be difficult to generate in situations with dose titration or informative dropout. Here, we present a novel simulation-free diagnostic tool for parametric RTTE models; the kernel-based visual hazard comparison (kbVHC). The kbVHC aims to evaluate whether the mean predicted hazard rate of a parametric RTTE model is an adequate approximation of the true hazard rate. Because the true hazard rate cannot be directly observed, the predicted hazard is compared to a non-parametric kernel estimator of the hazard rate. With the degree of smoothing of the kernel estimator being determined by its bandwidth, the local kernel bandwidth is set to the lowest value that results in a bootstrap coefficient of variation (CV) of the hazard rate that is equal to or lower than a user-defined target value (CVtarget). The kbVHC was evaluated in simulated scenarios with different number of subjects, hazard rates, CVtarget values, and hazard models (Weibull, Gompertz, and circadian-varying hazard). The kbVHC was able to distinguish between Weibull and Gompertz hazard models, even when the hazard rate was relatively low (< 2 events per subject). Additionally, it was more sensitive than the Kaplan-Meier VPC to detect circadian variation of the hazard rate. An additional useful feature of the kernel estimator is that it can be generated prior to model development to explore the shape of the hazard rate function.

Deformation Control of Highly Flexible Aircraft in Trimmed Flight and Gust Encounter

High-altitude long-endurance unmanned aerial vehicles are inherently highly flexible, and therefore are more susceptible to very large structural deformation in trimmed flight and in atmospheric turbulence. This work aims to avoid these large wing deformations by incorporating and deflecting multiple redundant control surfaces along the leading and trailing edges of the wing. These surfaces are used in both trim optimization to constrain wing deformation to a user-defined value in steady trimmed flight and in minimizing the dynamic response to gust encounter via a feedback controller. Trim optimization is performed using the simplex algorithm. Concurrently, a robust control scheme is designed and applied using loop shaping to demonstrate that, although the control surfaces are used for trimming the aircraft, they have enough authority left to effectively minimize elastic wing deformations during gust penetration. The aforementioned methodology is demonstrated on a highly flexible flying wing. Trim optimization effectively allows trimmed flight at various load factors in the flight envelope while maintaining small structural deformations. The designed controller significantly reduces the maximum wing deflection experienced by a gust disturbance, which is bounded below a user-specified value.

Friends and family: A software program for identification of unrelated individuals from molecular marker data.

The identification of related and unrelated individuals from molecular marker data is often difficult, particularly when no pedigree information is available and the data set is large. High levels of relatedness or inbreeding can influence genotype frequencies and thus genetic marker evaluation, as well as the accurate inference of hidden genetic structure. Identification of related and unrelated individuals is also important in breeding programmes, to inform decisions about breeding pairs and translocations. We present Friends and Family, a Windows executable program with a graphical user interface that identifies unrelated individuals from a pairwise relatedness matrix or table generated in programs such as coancestry and genalex. Friends and Family outputs a list of samples that are all unrelated to each other, based on a user-defined relatedness cut-off value. This unrelated data set can be used in downstream analyses, such as marker evaluation or inference of genetic structure. The results can be compared to that of the full data set to determine the effect related individuals have on the analyses. We demonstrate one of the applications of the program: how the removal of related individuals altered the Hardy-Weinberg equilibrium test outcome for microsatellite markers in an empirical data set. Friends and Family can be obtained from https://github.com/DeondeJager/Friends-and-Family.

WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces.

The concept of chemical space provides a convenient framework to analyze large collections of molecules by placing them in property spaces where distances represent similarities. Here we report webMolCS, a new type of web-based interface visualizing up to 5000 user-defined molecules in six different three-dimensional (3D) chemical spaces obtained by principal component analysis or similarity mapping of multidimensional property spaces describing composition (MQN: 42D molecular quantum numbers, SMIfp: 34D SMILES fingerprint), shapes and pharmacophores (APfp: 20D atom pair fingerprint, Xfp: 55D category extended atom pair fingerprint), and substructures (Sfp: 1024D binary substructure fingerprint, ECfp4:1024D extended connectivity fingerprint). Each molecule is shown as a sphere, and its structure appears on mouse over. The sphere is color-coded by similarity to the first compound in the list, by the list rank, or by a user-defined value, which reveals the relationship between any property encoded by these values and structural similarities. WebMolCS is freely available at www.gdb.unibe.ch .

Seismic time-frequency decomposition by using a hybrid basis-matching pursuit technique

Analyzing the time-frequency features of seismic traces plays an important role in seismic stratigraphy analysis and hydrocarbon detection. The current popular time-spectrum analysis methods include short-time Fourier transform, continuous wavelet transform, S-transform, and matching pursuit (MP), among which MP is the most tolerant of the window/scalar effect. However, current MP algorithms do not consider the interfering effects of seismic events on the estimation of optimal wavelets in each decomposition iteration. The interfered reflection seismic events may result in inaccurate estimation of optimal wavelets during the whole decomposition procedure. We have developed a hybrid basis MP workflow to minimize the effect of event interference on the estimation of optimal wavelets. Our algorithm assumes that the wavelet features remain constant in a user-defined small time window. The algorithm begins with identifying the strongest reflection waveform. Next, we estimate the optimal wavelet and the corresponding reflectivity model for the selected waveform by using a basis pursuit algorithm. Then, we subtract the seismic traces from the waveform computed from the optimal wavelet and estimated reflectivity model. We repeat this procedure until the total energy of seismic traces falls below a user-defined value. We have determined the effectiveness of our algorithm by first applying it to a synthetic model and then to a real seismic data set.