Deterministic Characteristics Research Articles

Effective fault detection in structural health monitoring systems

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

Reversible Mechanical Deformations of Soft Microchannel Networks for Sensing in Soft Robotic Systems

Microfluidics has enabled numerous applications in, for example, analytical chemistry, medical diagnostics, microelectronics, and soft robotics. In most of these applications, the geometries of the microchannels are of fixed dimensions that (ideally) remain invariant during operation. In soft robotics, however, the geometries of the microchannels contained in soft actuation systems are inherently dynamic, and the specific dimensions are expected to change during operation, and, by extension, the fluid transport properties of the system are variable. If this characteristic is not properly considered, or if methods are not developed to control it, the progress of soft robotic devices with distributed fluid transport systems can be hampered. Herein, the deformation of soft microchannel networks is investigated using a finite‐element method and experimental observations, and the understandings are applied to imbibe sensing capabilities in soft robots that have integrated microfluidic networks with dynamic channel geometries of predictable dimensions. This approach enables the fabrication of soft fluid transport systems with deterministic deformation characteristics—a capability that is specifically applied to touch and actuation sensing in soft actuators. This work provides insight into the channel deformation processes expected in soft robotic systems with embedded networks of microchannels, enabling devices with reliable/predictable transport properties.

Multiscale Gaussian process regression-based generalized likelihood ratio test for fault detection in water distribution networks

This paper proposes a new leak/contaminant detection approach that aims to enhance the monitoring of water distribution network (WDN). The developed method relies on using machine learning (e.g Gaussian process regression (GPR)) as a modeling framework and generalized likelihood ratio (GLRT) for detection purposes. To improve the performances of the developed GPR model even further, multiscale representation of data will be used to develop multiscale extension of these method. Multiscale representation is a powerful data analysis technique that presents efficient separation of deterministic characteristics from random noise. Therefore, the multiscale GPR method, that combines the advantages of the GPR method with those of multiscale representation, will be developed to enhance the WDN modeling performance. We develop a new technique for detecting leak/contaminant in WDN using GLRT. For further enhance, the performance of GLRT, an exponentially weighted moving average (EWMA)-GLRT (EWMA-GLRT) chart is developed. The simulation results show that the MSGPR-based EWMA-GLRT method outperforms MSGPR-based GLRT and that both of them provide clear advantages over the neural networks (NN)- and support vector regression (SVR)- and GPR-based GLRT techniques.

SWOT-analysis of digital technologies for an industrial enterprise

In the article, methods of SWOT-analysis of digital technologies for industrial enterprises are considered. It is shown that existing methods have disadvantages associated with obtaining objective and reliable expert estimates. To solve this problem, an approach is proposed to implement the idea of SWOT-analysis of technologies. This approach is based on the theory of system regularity and information assessments of A. A. Denisov. The research found that idea of SWOT-analysis of technologies that is based on the theory of system regularity and information assessments of A. A. Denisov, has a number of advantages, such as a convenient processing of estimates, the ability to combine probabilistic estimations with quantitative deterministic characteristics, which contributes to an increase in objectivity and reliability of estimates. Furthermore, it allows, based on changing measured deterministic parameters, to obtain the dynamics of changes in the degree of influence of sub-goals, factors, and means for the realization of the objectives of the enterprise.

Hierarchical 2-D Feature Coding for Secure Pilot Authentication in Multi-User Multi-Antenna OFDM Systems: A Reliability Bound Contraction Perspective

Due to the publicly known and deterministic characteristic of pilot tones, pilot authentication (PA) in multi-user multi-antenna orthogonal frequency-division multiplexing systems is very susceptible to the jamming/nulling/spoofing behaviors. To solve this, in this paper, we develop a hierarchical 2-D feature (H2DF) coding theory that exploits the hidden pilot signal features, i.e., the energy feature and independence feature, to secure pilot information coding which is applied between legitimate parties through a well-designed five-layer hierarchical coding model to achieve secure multiuser PA (SMPA). The reliability of SMPA is characterized using the identification error probability (IEP) of pilot encoding and decoding with the exact closed-form upper and lower bounds. However, this phenomenon of non-tight bounds brings about the risk of long-term instability in SMPA. Therefore, a reliability bound contraction theory is developed to shrink the bound interval, and practically, this is done by an easy-to-implement technique, namely, codebook partition within the H2DF code. In this process, a tradeoff between the upper and lower bounds of IEP is identified and a problem of optimal upper and lower bound tradeoff is formulated, with the objective of optimizing the cardinality of sub-codebooks such that the upper and lower bounds coincide. Solving this, we finally derive an exact closed-form expression for IEP, which realizes a stable and highly reliable SMPA. Numerical results validate the stability and resilience of H2DF coding in SMPA.

PHY-Layer Cover-Free Coding for Wireless Pilot Authentication in IoV Communications: Protocol Design and Ultra-Security Proof

Wireless channel state information (CSI) from intelligent vehicles to the roadside unit (RSU) is a must for vehicleto-infrastructure (V2I) communications in Internet of Vehicles, but easily suffers the risks of being attacked due to the publicly known and deterministic characteristic of PHY-layer pilots that are employed for CSI acquisition. This incurs the issue of wireless pilot authentication (WPA), that is, verifying the authenticity of pilots and claimed CSIs. In this paper, we, for multiantenna V2I orthogonal frequency division multiplexed communications, develop a PHY-layer cover-free (PHY-CF) coding theory to build up a secure WPA (SWPA) protocol. Here, we encode and convey vehicle pilot signals into diversified subcarrier activation patterns (SAPs) on the time-frequency domain by employing cover-free coding. We redesign the decoding procedure using the signal independence characteristic such that those encoded SAPs, though camouflaged by malicious signals and superimposed onto each other in wireless environment, could be separated, identified and decoded into the original pilots securely. For this protocol, we prove that perfect pilot conveying and separation could be both guaranteed. We formulate the pilot identification error probability (IEP) and show how PHY-CF coding could help position the location of attacker and reduce IEP to further achieve ultrasecurity. Considering 20 MHz long-term evolution bandwidth, we prove that the number of co-time co-frequency vehicles that are securely authenticated achieves up to 19 × X for X serving sectors of base station type RSU and the latency time of uplink data access is up to 1.5 ms, thus furthering the autonomous driving. Computer simulations comprehensively verify those benefits of proposed SWPA protocol.

Effective large for gestational age prediction using machine learning techniques with monitoring biochemical indicators

A newborn with a birth weight above the 90th percentile of same gestational age is termed as large for gestational age. Large for gestational age suffers from serious complications during and after the antepartum period because they do not get earlier identification of the disease. Earlier recognition of large for gestational age infants could slow progression and prevent further complication of the disease. In medical science, prevention and mitigation of disease require examination of biochemical indicators. Machine learning has been evolved and envisioned as a tool to predict large for gestational age infants with most deterministic characteristics. This study aims to identify most deterministic biochemical indicators for large for gestational age prediction with minimal computational overhead. To the best of my knowledge, this is the first time a study is carried out to identify the most deterministic risk factors associated with large for gestational age and to develop large for gestational age prediction model using machine learning techniques. To develop an efficient large for gestational age prediction model, we conducted three group of experiments that considered basic machine learning methods; feature selection; and imbalanced data, respectively. Support vector machine, logistic regression, Naive Bayes and Random Forest were trained using tenfold cross-validation on large for gestational age dataset; we selected precision and area under the curve as a performance evaluation metrics; information gain an entropy-based feature selection method was adopted to rank features; we introduced an ensemble data imbalance technique in the last group of experiments. For each group of experiments, support vector machine performed best compared to other machine learning classifiers by producing the highest prediction precision score of 85%. All of the classifiers performed best with thirty ranked features subset, which validates the applied method to recognize the most deterministic risk factors associated with large for gestational age prediction.

Research on high deterministic removal characteristics of fused quartz using RF focused ion beam source

Research on high deterministic removal characteristics of fused quartz using RF focused ion beam source

Modeling of construction technology of objects on the basis of technological interaction of works

The problems of sustainability and stability of organizational and technological models of the object construction are revealed. The scientific approach is generalized for the formation of the complex properties of organizational and technological models, based on the consideration of the technology of object construction as a framework for the development of construction models. The technology of construction of buildings and structures is analyzed. The method of the consideration of technologies for the construction of buildings and structures is proposed from the point of view of the connections between technologically interconnected processes and the nature of the interaction of linked simple and complex processes. The dependences between technologically related technological activities and processes are obtained. The qualitative and quantitative estimates of the dependences obtained between technologically related works and processes are determined. The deterministic characteristics of the technical connections between works and processes, giving steady properties and used for the development of object construction models.

Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein.

Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarckian genetic algorithm (LGA) as energy optimization algorithms. The best conformations of each docking algorithm were subjected to molecular dynamic (MD) simulations to further analyze the molecular mechanisms of protein-ligand interactions. Here, we analyze the binding energy between protein receptors and ligands, the interactions of salt bridges and hydrogen bonds in the docking region, and the structural changes during complex unfolding. Our comparison of these complexes highlights differences in the protein-ligand interactions between the two docking methods. It also shows that salt bridge and hydrogen bond interactions play a crucial role in protein-ligand stability. The present work focuses on extracting the deterministic characteristics of docking interactions from their dynamic properties, which is important for understanding biological functions and determining which amino acid residues are crucial to docking interactions.

K-NN and ANN based deterministic and probabilistic wind speed forecasting intelligent approach

Development of power through wind with the enhancement of renewable energy resources, frolics/romps a principal role in a developing country like India due to its censorious locations. Wind speed prediction in long term scenario has become a key research area in distinct applications (i.e., managem ent of energy, optimal designing of wind farm, restructuring of electricity marketing, load-shedding and load forecasting). However, forecasting of accurate wind speed data for installation of wind turbine is very difficult due to its deterministic and probabilistic characteristics. The presented technique in this study may bridge the research gap related with the long term wind speed forecasting as resolve the previously indicated problems. Thence, two basically distinct techniques, k-nearest neighbors (kNN) algorithm and artificial neural network (ANN), have been implemented to forecasting of monthly wind speed of Indian cities. The uniqueness of the presented paper is to predict the wind speed in common form of incoming month by implementing the kNN algorithm. A dataset of current wind speed recorded specimen from 168 cities of India is utilized to train and test the proposed approach. Obtained results through the proposed approach have been validated by using ANN technique, which shows very small MSE.

The formalized approach of building a network controller for the information handling automated process control system of the technological control cycle

The article discusses the use of a formalized approach to building a network controller which bases on a graph of Mealy machine. This approach can be applied in modern automated control systems (ACS), since ACS can be considered as network systems, intended for transportation (transmission) and distribution of flows, including energy and information. A comparison of a fragment of the graph of Mealy machine and a fragment of the flowchart of the algorithm and basic interpretations of their elements is presented. The technological process of applying protective coatings is considered, as well as basic operations that are deterministic. Considered minimization of the state of the graph automaton and the transformation into the flowchart of the technological process operations is obtained. The proposed approach allows obtaining the deterministic characteristics of the information handling automated process control system of the technological control cycle.

Modeling of construction technology of objects on the basis of technological interaction of works

The problems of sustainability and stability of organizational and technological models of the object construction are revealed. The scientific approach is generalized for the formation of the complex properties of organizational and technological models, based on the consideration of the technology of object construction as a framework for the development of construction models. The technology of construction of buildings and structures is analyzed. The method of the consideration of technologies for the construction of buildings and structures is proposed from the point of view of the connections between technologically interconnected processes and the nature of the interaction of linked simple and complex processes. The dependences between technologically related technological activities and processes are obtained. The qualitative and quantitative estimates of the dependences obtained between technologically related works and processes are determined. The deterministic characteristics of the technical connections between works and processes, giving steady properties and used for the development of object construction models.

Radiation Heat Transfer Through Carbon Fiber Materials: Experiment vs Theory

Comparisons between theoretical predictions and test data for two fiber materials containing sparsely dispersed carbon fibers are presented in this Paper. Heat transfer through the test specimens was measured in vacuum from to 1100 K. The theoretical predictions are based on Lee’s radiation model for a fibrous medium that uses only deterministic characteristics of the material composition. Good agreement is observed between the predictions and test data. The present study, in conjunction with other published comparisons for different types of fiber materials, provides an additional validation of Lee’s radiation model for fiber media.

Refining ionospheric delay modeling for undifferenced and uncombined GNSS data processing

To access the full capabilities of multi-frequency signals from the modernized GPS, GLONASS and newly deployed BDS, Galileo, the undifferenced and uncombined observable model in which the individual signal of each frequency is treated as independent observable has drawn increasing interest in GNSS community. The ionosphere delay is the major issue in the undifferenced and uncombined observable model. Though several ionosphere delay parameterization approaches have been promoted, we argue that the functional model with only deterministic characteristic may not follow the irregular spatial and temporal variations. On the contrary, when the ionosphere delay is estimated as random walk or even white noise with only stochastic characteristic, the ionosphere terms turn out to be non-estimable or not sensitive to their absolute value. In the authors’ previous study, we have developed the deterministic plus stochastic ionosphere model, denoted as DESIGN, in which the deterministic part expressed with second-order polynomial is estimated as piece-wise constant over 5 min and the stochastic part is estimated as random walk with constrains derived based on statistics of 4 weeks data in 2010. In this contribution, we further model the deterministic part with Fourier series and update the variogram of the stochastic part accordingly based on two-year data collected by about 150 stations. From the statistic studies, it is concluded that the main frequency components are identical for different coefficients, different stations, as well as different ionosphere activity status, but with varying amplitude. Thus, in the Fourier series expression of the deterministic part, we fix the frequency and estimate the amplitude as daily constant unknowns. Concerning the stochastic component, the variation of variogram is both, geomagnetic latitude and ionosphere activity status dependent. Thus, we use the Gaussian function and Epstein function to model the variation of geomagnetic latitude and ionosphere activity status, respectively. Based on the undifferenced and uncombined observable model with ionosphere constrained with DESIGN, both dual-frequency and single-frequency PPP are carried out to demonstrate its efficiency with three-month data collected in 2010, 2014, and 2017 with different ionosphere activity status. The experimental results suggest that compared with ionosphere-free model and our previous method, the averaged 3D improvement of our new method is 17.8 and 7.6% for dual-frequency PPP, respectively. While for single-frequency PPP, the averaged 3D improvement is 37.0 and 14%, respectively.

Wavelet optimized EWMA for fault detection and application to photovoltaic systems

Electrical power generation using photovoltaic (PV) became an active and continuous growing area for academic and industrial research. The complexity of PV systems and the increase in reliability requirement become a very important issue in automation. Grid-connected PV systems are among the top power technologies with the highest rate of development. Therefore, their proper operation and safe handling is a top priority. To respond for this exigency, we develop a novel technique for PV power systems monitoring. Various key variables can be monitored in PV systems, which include the voltage and frequency of the grid, the voltage and the current of the AC and DC converters, as well as climate data, such as the temperature and irradiance. Tight monitoring of these variables will provide more effective and less interrupted energy supplies. The developed monitoring method is applied and validated using simulated data of PV systems. The developed technique combines the advantages of Exponentially Weighted Moving Average (EWMA), multi-objective optimization (MOO) and Wavelet representation. The MOO is used here to solve the problem of choosing an optimal solution of the following two objective functions: (i) missed detection rate (MDR) and (ii) false alarm rate (FAR) where both of them are simultaneously minimized. Additionally, the use of wavelet representation improves the monitoring performances by reducing the MDR and FAR. The wavelet representation is applied to obtain precise deterministic characteristics besides decorrelation of autocorrelated measurements. The new proposed technique, called Wavelet Optimized EWMA (WOEWMA), is compared with the classical EWMA and Shewhart charts where they are used for detecting single and multiple faults (for example, Bypass, Mismatch, Mix and Shading faults). The performances of the monitoring scheme are evaluated using MDR and FAR indicators.

WinProGen: A Markov-Chain-based stochastic window status profile generator for the simulation of realistic energy performance in buildings

New and retrofitted buildings often do not perform as expected. In fact, the real energy performance of a building depends on deterministic characteristics (e.g. building's structure and HVAC), and on stochastic elements (e.g. occupants' behavior). Probabilistic models of occupant behavior in the simulation of buildings' energy performance can help to bridge the gap between prediction and real energy consumption. With this aim, a stochastic window status profile generator (WinProGen) is introduced, validated (using the Markov chain Monte Carlo technique) through observations from field tests, and tested through dynamic building simulations. In WinProGen, we implemented three models for the generation of window state profiles, based on field test data, with a time resolution of 1 min. The profiles generated from model 1 depend on the time of the day and the daily average ambient temperature. The profiles generated from model 2 depend on the time of the day, on the daily average ambient temperature and on the day of the week (working day or weekend day). The profiles generated from model 3 depend on the time of the day, on the daily average ambient temperature of the actual day and on the daily average ambient temperature of the past day. The generated profiles can be used as an input to simulate dynamic building energy performance. Moreover, users can include in WinProGen their own field test data to generate own state profiles. The dynamic simulation of two demonstrator buildings with the generated window state profiles offers reliable predictions of buildings' energy performance.

Patterned electromagnetic alignment of magnetic nanowires

A combination of electromagnetic alignment and topological pattern assisted alignment to position magnetic nanowires, which is referred to as the Patterned Electromagnetic Alignment (PEA), is developed and examined. Electrodeposited, FeNiCo nanowires with different lengths were used as the test nanomaterial, and the microscale grooved surface was formed by UV nanoimprint lithography. The accuracy of the PEA with FeNiCo nanowires was evaluated by measuring the deviation angle from the direction of the magnetic field line for different magnetic field strengths and nanowire lengths, and a statistical alignment distribution was reported for different nanowire length groups. The results were compared with those of the electromagnetic alignment on flat surfaces and in grooved-patterned substrates without electromagnetic alignment. Overall, the deviation angle for the PEA was lower than that for the electromagnetic alignment when all other experimental conditions were identical, indicating that the alignment accuracy along the direction of the magnetic field lines was enhanced in the presence of surface micro grooves. This can be attributed to the fact that, upon attachment of nanowires to the substrate surface, the surface micro grooves in the PEA add additional deterministic characteristics to the otherwise stochastic nature of the nanowire deposition and solvent evaporation processes compared to the sole electromagnetic alignment.

Characterization of forced response of density stratified reacting wake

The hydrodynamic stability of a reacting wake depends primarily on the density ratio [i.e., ratio of unburnt gas density (ρu) to burnt gas density (ρb)] of the flow across the wake. The variation of the density ratio from high to low value, keeping ρu/ρb>1, transitions dynamical characteristics of the reacting wake from a linearly globally stable (or convectively unstable) to a globally unstable mode. In this paper, we propose a framework to analyze the effect of harmonic forcing on the deterministic and synchronization characteristics of reacting wakes. Using the recurrence quantification analysis of the forced wake response, we show that the deterministic behaviour of the reacting wake increases as the amplitude of forcing is increased. Furthermore, for different density ratios, we found that the synchronization of the top and bottom branches of the wake with the forcing signal is dependent on whether the mean frequency of the natural oscillations of the wake (fn) is lesser or greater than the frequency of external forcing (ff). We notice that the response of both branches (top and bottom) of the reacting wake to the external forcing is asymmetric and symmetric for the low and high density ratios, respectively. Furthermore, we characterize the phase-locking behaviour between the top and bottom branches of the wake for different values of density ratios. We observe that an increase in the density ratio results in a gradual decrease in the relative phase angle between the top and bottom branches of the wake, which leads to a change in the vortex shedding pattern from a sinuous (anti-phase) to a varicose (in-phase) mode of the oscillations.

In Search of Determinism-Sensitive Region to Avoid Artefacts in Recurrence Plots

As an effort to reduce parameter uncertainties in constructing recurrence plots, and in particular to avoid potential artefacts, this paper presents a technique to derive artefact-safe region of parameter sets. This technique exploits both deterministic (incl. chaos) and stochastic signal characteristics of recurrence quantification (i.e. diagonal structures). It is useful when the evaluated signal is known to be deterministic. This study focuses on the recurrence plot generated from the reconstructed phase space in order to represent many real application scenarios when not all variables to describe a system are available (data scarcity). The technique involves random shuffling of the original signal to destroy its original deterministic characteristics. Its purpose is to evaluate whether the determinism values of the original and the shuffled signal remain closely together, and therefore suggesting that the recurrence plot might comprise artefacts. The use of such determinism-sensitive region shall be accompanied by standard embedding optimization approaches, e.g. using indices like false nearest neighbor and mutual information, to result in a more reliable recurrence plot parameterization.