Relevant Dynamical Features Research Articles

A Novel Distributed CVRAE-Based Spatio-Temporal Process Monitoring Method With Its Application

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy and sewage treatment. In this paper, a novel spatio-temporal monitoring method is proposed for the hot strip mill process, a typical tandem industrial process. Firstly, the plant-wide process is divided into different sub-blocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder (CVRAE)-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description (SVDD) to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a hot strip mill (HSM) process case, which shows better monitoring performance compared to the existing methods.

Dynamics of curved domain walls in hard ferromagnets with nonlinear dissipative and inertial effects

This article investigates the field-driven motion of curved domain walls in ferromagnetic nanostructures under the framework of modified Landau–Lifshitz–Gilbert equation with inertial effects. The considered governing equation involves the torques arising from nonlinear dissipative (viscous and dry friction) and inertial effects. We study the most relevant dynamical features in the steady dynamical regime for the considered model by employing the reductive perturbation technique. Finally, we illustrate the results numerically for the various domain wall surfaces (plane, cylinder, and sphere) and discuss their physical significance. The results obtained here agree with the recent theoretical and experimental observations.

Monitoring and control of the program comprehension teaching in a computer class environment

Introduction. The comprehension of computer programs is claimed to be a decisive faculty and even skill of any successful exercising professional duties of program engineers, for example, prototype analysis, decision making and discussion about algorithms and structures, development of detailed documentation, test development and test analysis. Exercising of the duties demands from the engineer a special competence to judge about a program behavior basing on the consideration of its source and sometimes digital codes, screenshots, execution protocols. The skill and opportunity to make trial runs can supplement but not replace the competence. Manifesting itself on particular programs it is not a presentation of an extensive and deep familiarity with the programs, but results from the experience with a lot of programs and is obscured by another competence, the competence of the developer. This paper is aimed to precise the interpretation of the notion “program comprehension”, to research the opportunities of a purposeful and enhanced teaching the program comprehension, to develop methods and tools for the assessment of the program comprehension teaching in the higher school. The aim is relevant because of current university curricula allot considerable lecture and laboratory time for teaching programming, including program comprehension. But this time is constantly not enough in view of a growing intensity of innovations in the area of the program engineering. The problem of a control over processes of the program comprehension fostering in a computer class is a key problem of achieving the aim having been set. Materials and methods. The research is based on the university syllabi, the author’s lecture notes, protocols of experimental and sample runs of the developed programs, Internet resources, such as site of the ACM and others. The following research methods were used: terminological analysis and elaboration of a vocabulary demanded in the research and discourse within the paper; modelling and simulation, including a theoretical conceptual model representation of the program comprehension, a computer program simulation of the monitoring and control over the comprehension progress; an UMLpresentation and a trial implementation of the proposed interface for the program comprehension monitoring and control. Research results. Redefinition of conceptual cognitive models of program comprehension is proposed. The program comprehension is postulated to be a state of the cognitive knowledge base that represents an actual affordance of giving correct answers to questions about an eventual program behavior. This model highlights a special kind of teaching to be called “didactic parsing” that is targeted to generate at the audience a comprehension of programs being parsed. Dynamics of question and answers during the didactic parsing is used for assessing the education success and, finally, for a control of the teaching tempo. A user interface for learning computer systems (counters, controls and policies) are proposed to assist in monitoring and control of the didactic parsing. Sample implementation of the interface is built in JL (Joint Lesson) computer system. A graph model and a corresponding simulation program are developed that capture relevant dynamic features of control over the didactic parsing and are applied for the further investigation and demonstration of general didactic parsing effects as well as an aid for planning and control of program comprehension teaching during particular lectures. Conclusion. The research results provide creation of an environment, methods and tools enabling to positively impact the processes of program comprehension attainment. In particular, they permit to control the program comprehension level at a particular audience, adjust style of teaching to be most applicable to it, refine time plans of lectures in the area of program engineering.

Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms.

Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and locker ransomware from goodware. (3) Generate and make public a dynamic features dataset that includes these selected parameters for samples of different artifacts. (4) Apply the dynamic feature dataset to obtain models with machine learning algorithms. Five platforms, 20 ransomware, and 20 goodware artifacts were evaluated. The final feature dataset is composed of 2000 registers of 50 characteristics each. This dataset allows for a machine learning detection with a 10-fold cross-evaluation with an average accuracy superior to 0.99 for gradient boosted regression trees, random forest, and neural networks.

Utilizing motion and spatial features for sign language gesture recognition using cascaded CNN and LSTM models

Sign language is a language produced by body parts gestures and facial expressions. The aim of an automatic sign language recognition system is to assign meaning to each sign gesture. Recently, several computer vision systems have been proposed for sign language recognition using a variety of recognition techniques, sign languages, and gesture modalities. However, one of the challenging problems involves image preprocessing, segmentation, extraction and tracking of relevant static and dynamic features related to manual and nonmanual gestures from different images in sequence. In this paper, we studied the efficiency, scalability, and computation time of three cascaded architectures of convolutional neural network (CNN) and long short-term memory (LSTM) for the recognition of dynamic sign language gestures. The spatial features of dynamic signs are captured using CNN and fed into a multilayer stacked LSTM for temporal information learning. To track the motion in video frames, the absolute temporal differences between consecutive frames are computed and fed into the recognition system. Several experiments have been conducted on three benchmarking datasets of two sign languages to evaluate the proposed models. We also compared the proposed models with other techniques. The attained results show that our models capture better spatio-temporal features pertaining to the recognition of various sign language gestures and consistently outperform other techniques with over 99% accuracy.

Dynamic mode decomposition of mixing characteristics in a T-junction with a rotation impeller

Coherent structures of turbulent mixing between cold and hot waters in a T-junction with a rotation impeller with varying blade numbers (Np = 2–4) are studied using the dynamic mode decomposition method. Velocity and temperature fields are obtained using large eddy simulation at the deflecting flow pattern classified by momentum ratio (MR = 0.49). Relevant dynamic features of a flow with low frequency mainly appear near the impeller and those with high frequency appear at the upper and bottom walls. The maximum velocity fluctuation increases with the blade number. Vortex shedding occurs at the blade tips. Spatial structures of the temperature field are mainly distributed at the mixing interface of the two fluids and impeller region. At Np = 2 and 3, energetic coherent structures appear at the bottom wall, while at Np = 4, they appear at the upper and bottom walls. The maximum temperature fluctuation first increases and then decreases with increasing blade number. The frequencies of velocity and temperature modes comprise multiple blade passing frequency (BPF) and rotation frequency, and the effect of rotation frequency on the velocity and temperature modes disappears with increasing the blade number. Furthermore, the frequency of the temperature mode is higher than that of velocity at Np ≤ 3 except for the identical BPF; Np = 4, the two frequencies tend to be more synchronous.

Eckhaus instability of stationary patterns in hyperbolic reaction–diffusion models on large finite domains

We have theoretically investigated the phenomenon of Eckhaus instability of stationary patterns arising in hyperbolic reaction–diffusion models on large finite domains, in both supercritical and subcritical regime. Adopting multiple-scale weakly-nonlinear analysis, we have deduced the cubic and cubic–quintic real Ginzburg–Landau equations ruling the evolution of pattern amplitude close to criticality. Starting from these envelope equations, we have provided the explicit expressions of the most relevant dynamical features characterizing primary and secondary quantized branches of any order: stationary amplitude, existence and stability thresholds and linear growth rate. Particular emphasis is given on the subcritical regime, where cubic and cubic–quintic Ginzburg–Landau equations predict qualitatively different dynamical pictures. As an illustrative example, we have compared the above-mentioned analytical predictions to numerical simulations carried out on the hyperbolic modified Klausmeier model, a conceptual tool used to describe the generation of stationary vegetation stripes over flat arid environments. Our analysis has also allowed to elucidate the role played by inertia during the transient regime, where an unstable patterned state evolves towards a more favorable stable configuration through sequences of phase-slips. In particular, we have inspected the functional dependence of time and location at which wavelength adjustment takes place as well as the possibility to control these quantities, independently of each other.

Compositeness and several applications to exotic hadronic states with heavy quarks

Several methods for studying the nature of a resonance are applied to resonances recently discovered in the bottonomium and charmonium sectors. We employ the effective-range expansion, the saturation of the width and compositeness of a resonance, as well as direct fits to data. The latter stem from generic S -matrix parameterization that account for relevant dynamical features associated to channels that couple strongly in an energy region around the resonance masses, in which their thresholds also lie. We report on results obtained with these methods for the resonances Zb(10610), Zb(10650), Zcs(3985), Zc(3900), X(4020), X(6900), X(6825), and Pcs(4459).

Small-signal analysis and control implementation of boost converter fed PMDC motor for electric vehicle applications

Small-signal analysis of boost converter fed permanent magnet DC (PMDC) motor for electric vehicle applications is performed, and hardware implementation is realized in this paper. Extensive analysis is performed to identify the relevant steady-state and dynamic features of the proposed system with small-signal linearization, and relevant transfer functions are formulated. The nonlinear equations of the system are derived and then linearized around a stable operating point to construct a small-signal model. Transfer functions relating the control of the converter to the motor speed and control to input current are derived symbolically using computerized symbolic algebra in MathCAD. The control-to-output transfer functions are obtained by introducing perturbation in state variables, equating AC and DC quantities and proceeding with AC quantities. The principle of operation, operation modes, small-signal analysis, experimental verification, and the effectiveness of the speed control are discussed and presented. An experimental prototype is implemented using dSPACE DS1103-based digital signal processor, and the proposed model is used for online parameter tuning of the speed controller. The speed control dynamics and transient response are investigated under sudden load changes. The overall system performance is evaluated and verified experimentally based on a speed feedback control scheme for validation purposes.

Detection and robustness evaluation of android malware classifiers

Android malware attacks are tremendously increasing, and evasion techniques become more and more effective. For this reason, it is necessary to continuously improve the detection performances. With this paper, we wish to pursue this purpose with two contributions. On one hand, we aim at evaluating how improving machine learning-based malware detectors, and on the other hand, we investigate to which extent adversarial attacks can deteriorate the performances of the classifiers. Analysis of malware samples is performed using static and dynamic analysis. This paper proposes a framework for integrating both static and dynamic features trained on machine learning methods and deep neural network. On employing machine learning algorithms, we obtain an accuracy of 97.59% with static features using SVM, and 95.64% is reached with dynamic features using Random forest. Additionally, a 100% accuracy was obtained with CART and SVM using hybrid attributes (on combining relevant static and dynamic features). Further, using deep neural network models, experimental results showed an accuracy of 99.28% using static features, 94.61% using dynamic attributes, and 99.59% by combining both static and dynamic features (also known as multi-modal attributes). Besides, we evaluated the robustness of classifiers against evasion and poisoning attack. In particular comprehensive analysis was performed using permission, APIs, app components and system calls (especially n-grams of system calls). We noticed that the performances of the classifiers significantly dropped while simulating evasion attack using static features, and in some cases 100% of adversarial examples were wrongly labelled by the classification models. Additionally, we show that models trained using dynamic features are also vulnerable to attack, however they exhibit more resilience than a classifier built on static features.

Functional modeling of recurrent events on time-to-event processes.

In clinical practice, it is often the case where the association between the occurrence of events and time-to-event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account their possibly time-varying nature, as well as to provide a new setting for quantifying the association between time-varying processes and time-to-event outcomes. We propose an innovative methodology to model information carried out by time-varying processes by means of functional data, modeling each time-varying variable as the compensator of marked point process the recurrent events are supposed to derive from. By means of Functional Principal Component Analysis, a suitable dimensional reduction of these objects is carried out in order to plug them into a Cox-type functional regression model for overall survival. We applied our methodology to data retrieved from the administrative databases of Lombardy Region (Italy), related to patients hospitalized for Heart Failure (HF) between 2000 and 2012. We focused on time-varying processes of HF hospitalizations and multiple drugs consumption and we studied how they influence patients' overall survival. This novel way to account for time-varying variables allowed to model self-exciting behaviors, for which the occurrence of events in the past increases the probability of a new event, and to quantify the effect of personal behaviors and therapeutic patterns on survival, giving new insights into the direction of personalizedtreatment.

Methods for ray-tracing thermal modelling of Saturn's main rings

The thermal emission and temperatures of the main rings of Saturn depend on the energy the ring particles absorb, reflect and scatter and/or on their Bond albedo, emissivity, thermal inertia, rotation rate and porosity. However, the energy that each particle absorbs also depends on the amount of energy (e.g., solar energy) that reaches its surface and this latter on the local optical depth, that controls the mutual eclipsing between neighbouring particles and, in general, all shadowing effects on the rings. On the other hand, thermal models of the rings of Saturn based on the energy balance equation strongly depend on a function that described how the non-shadowed area of ring particles changes with solar elevation. Experimental and analytical shadowing functions have been proposed by [6] and [1], respectively. In this work, we propose shadowing functions based on the creation of 3D arrays of spherical particles that simulate specific regions of the main rings of Saturn. The methods implemented to obtain these shadowing functions follow the next general steps:• Arrays are created as a collection of spherical particles with a size distribution that follows a power law constrained to the optical depth of the region of study based on the UVIS instrument data.• The particles of the arrays are then reordered to add some relevant dynamical features observed in actual rings (e.g., wake structures in the case of optically-thick rings).• Under different illumination geometries, images of these arrays are rendered using ray tracing. From these images, an analysis of their pixel brightness values allows us to determine the non-shadowed fractional area of the particles in order to compose the corresponding shadowing functions.

A TAN based hybrid model for android malware detection

Android devices are very popular because of their availability at reasonable prices. However, there is a rapid rise of malware applications in Android platform in the recent past years due to its security vulnerabilities. The existing static malware detection mechanisms can locate malicious components associated with the source code of an application and dynamic analysis can identify exploits in the runtime environment. Hence, the advantages of both static and dynamic mechanisms need to be combined to form a hybrid analysis mechanism for achieving better accuracy in malware detection. The existing machine learning based hybrid malware analysis mechanisms do not check the interdependency of static and dynamic features used in their machine learning classifiers. This interdependency can lead to multicollinearity problem which can affect the classifier’s performance. Hence, in this paper we propose a novel TAN (Tree Augmented naive Bayes) based hybrid malware detection mechanism by employing the conditional dependencies among relevant static and dynamic features (API calls, permissions and system calls) which are required for the functionality of an application. We trained three ridge regularized logistic regression classifiers corresponding to API calls, permission and system calls of an application and modeled their output relationships as a TAN (Tree Augmented naive Bayes) for identifying whether the application is malicious or not. The experimental results show that the proposed mechanism can detect malicious applications over a long period with an accuracy of 0.97.

A tale of two vortices: How numerical ergodic theory and transfer operators reveal fundamental changes to coherent structures in non-autonomous dynamical systems

Coherent structures are spatially varying regions which disperse minimally over time and organise motion in non-autonomous systems. This work develops and implements algorithms providing multilayered descriptions of time-dependent systems which are not only useful for locating coherent structures, but also for detecting time windows within which these structures undergo fundamental structural changes, such as merging and splitting events. These algorithms rely on singular value decompositions associated to Ulam type discretisations of transfer operators induced by dynamical systems, and build on recent developments in multiplicative ergodic theory. Furthermore, they allow us to investigate various connections between the evolution of relevant singular value decompositions and dynamical features of the system. The approach is tested on models of periodically and quasi-periodically driven systems, as well as on a geophysical dataset corresponding to the splitting of the Southern Polar Vortex.

Study on instability of circular liquid jets at subcritical to supercritical conditions using dynamic mode decomposition

The thermodynamic and fluid-dynamic properties associated with the injectant jet significantly influence its fluid dynamic characteristics. In most of the earlier studies on the behavior of liquid jet at high pressure and supercritical environment, the liquid jet is often injected at subcritical initial temperature. In the present study, the effect of variation in the fluid and flow properties of the injecting jet on the nature of instabilities and mixing behavior is investigated. The fluid properties of the injected jet are varied by preheating. High-speed imaging techniques are used to capture the jet behavior. To gain further insight into the instability nature of the jet, linear instability analysis was performed. Dynamic mode decomposition analysis was applied to the obtained high-speed images to extract and understand the relevant dynamic features.

The dynamic lexicon in a truth-conditional framework; or how to have Your cake and eat it

ABSTRACTA fundamental principle of all truth-conditional approaches to semantics is that the meanings of sentences of natural language can be compositionally specified in terms of truth conditions, where the meanings of the sentences’ parts (words/lexical items) are specified in terms of the contribution they make to such conditions their host sentences possess. Thus, meanings of words fit the meanings of sentences at least to the extent that the stability of what a sentence might mean as specified in a theory is in accord with the stability of what a word might mean as similarly specified. In this paper, I shall be concerned with Ludlow’s (2014) idea that, in fact, there need be no such sympathy between words and sentences. He proposes that we can square what he calls a dynamic lexicon, where word meaning is not stable at all, with a traditional truth-conditional approach of the kind indicated, where sentence meaning is delivered via ‘absolute truth conditions’. I share Ludlow’s aspiration to accommodate dynamic features of word meaning with a truth conditional approach, but not his belief that the marriage is an easy deal. Thus, I shall present a problem for Ludlow’s position and show how resolving this problem leads to an alternative picture of how the meaning of a sentence may be truth-conditionally specified with all relevant dynamic features of the lexicon retained.

Measuring the dynamic photosynthome.

BackgroundPhotosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.ScopeIt is known that photosynthesis does not respond in a timely manner, and even a small temporal ‘mismatch’ between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.ConclusionsThere is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

Coacervation-Mediated Combinatorial Synthesis of Biomatrices for Stem Cell Culture and Directed Differentiation.

Combinatorial screening represents a promising strategy to discover biomaterials for tailored cell culture applications. Although libraries incorporating different biochemical cues have been investigated, few simultaneously recapitulate relevant biochemical, physical, and dynamic features of the extracellular matrix (ECM). Here, a noncovalent system based on liquid-liquid phase separation (coacervation) and gelation mediated by glycosaminoglycan (GAG)-peptide interactions is reported. Multiple biomaterial libraries are generated using combinations of sulfated glycosaminoglycans and poly(ethylene glycol)-conjugated peptides. Screening these biomaterials reveals preferred biomatrices for the attachment of six cell types, including primary mesenchymal stromal cells (MSCs) and primary neural precursor cells (NPCs). Incorporation of GAGs sustains the expansion of all tested cell types comparable to standard cell culture surfaces, while osteogenic differentiation of MSC and neuronal differentiation of NPC are promoted on chondroitin and heparan biomatrices, respectively. The presented noncovalent system provides a powerful tool for developing tissue-specific ECM mimics.

Assessment of signature handwriting evidence via score-based likelihood ratio based on comparative measurement of relevant dynamic features

This paper extends on previous research on the extraction and statistical analysis on relevant dynamic features (width, grayscale and radian combined with writing sequence information) in forensic handwriting examinations. In this paper, a larger signature database was gathered, including genuine signatures, freehand imitation signatures, random forgeries and tracing imitation signatures, which are often encountered in casework. After applying Principle Component Analysis (PCA) of the variables describing the proximity between specimens, a two-dimensional kernel density estimation was used to describe the variability of within-genuine comparisons and genuine–forgery comparisons. We show that the overlap between the within-genuine comparisons and the genuine–forgery comparisons depends on the imitated writer and on the forger as well. Then, in order to simulate casework conditions, cases were simulated by random sampling based on the collected signature dataset. Three-dimensional normal density estimation was used to estimate the numerator and denominator probability distribution used to compute a likelihood ratio (LR). The comparisons between the performance of the systems in SigComp2011 (based on static features) and the method presented in this paper (based on relevant dynamic features) showed that relevant dynamic features are better than static features in terms of accuracy, false acceptance rate, false rejection rate and calibration of likelihood ratios.

Tropical Cyclones in the 7km NASA Global Nature Run for use in Observing System Simulation Experiments.

The National Aeronautics and Space Administration (NASA) Nature Run (NR), released for use in Observing System Simulation Experiments (OSSEs), is a 2-year long global non-hydrostatic free-running simulation at a horizontal resolution of 7 km, forced by observed sea-surface temperatures (SSTs) and sea ice, and inclusive of interactive aerosols and trace gases. This article evaluates the NR with respect to tropical cyclone (TC) activity. It is emphasized that to serve as a NR, a long-term simulation must be able to produce realistic TCs, which arise out of realistic large-scale forcings. The presence in the NR of the realistic, relevant dynamical features over the African Monsoon region and the tropical Atlantic is confirmed, along with realistic African Easterly Wave activity. The NR Atlantic TC seasons, produced with 2005 and 2006 SSTs, show interannual variability consistent with observations, with much stronger activity in 2005. An investigation of TC activity over all the other basins (eastern and western North Pacific, North and South Indian Ocean, and Australian region), together with relevant elements of the atmospheric circulation, such as, for example, the Somali Jet and westerly bursts, reveals that the model captures the fundamental aspects of TC seasons in every basin, producing realistic number of TCs with realistic tracks, life spans and structures. This confirms that the NASA NR is a very suitable tool for OSSEs targeting TCs and represents an improvement with respect to previous long simulations that have served the global atmospheric OSSE community.