Application In Real Systems Research Articles

Characteristics of a laser system in complex field and its complex self-synchronization

Chaotic dynamics play a vital role in real secure communication and image encryption. This paper focuses on the characteristic analysis and complex self-synchronization (CSS) of a laser system in complex field. Its dynamical features are described by Lyapunov exponent spectrum, bifurcation diagram, phase portrait and the basin of attraction. The result of the investigation shows some attractive dynamical behaviors such as two different bifurcation routes, coexistence of axis-symmetric attractors, intermittent chaos and infinite transition of period and sink. In particular, two chaotic attractors with different topological structures are implemented on digital signal processor platform. Finally, the CSS is defined, and a novel scheme is proposed to achieve homogeneous and heterogeneous CSS of the laser complex-variable chaotic systems in complex field. The researches provide basis for the laser system in real applications.

Computer-aided prediction of hearing loss based on auditory perception

Hearing loss is the principle cause behind hearing impairment and deafness around the world. It is a significant health problem affecting our world today and it is growing exponentially among young and adults due to aging, hearing impairment, listening to music and noise exposure. Treatment options for hearing loss are very limited and it can only be controlled by taking precautions in the early stages. Thus, proper education is required to spread awareness among people to use hearing protections, reduce exposure to loud music and other risk factors. In this paper, we present a computer-aided method to predict and then prevent hearing loss. Three predictive models (human age estimation, prediction of hearing loss and level of hearing loss) based on auditory perception are presented. Our predictive model of human age estimation is very robust with a Mean Absolute Error (MAE) value of 4.1 years, while the accuracy of the second predictive model of hearing loss is equal to 94% and the third predictive model of the level of hearing loss present an accuracy of 90%. Our computer-aided prediction methods of hearing loss is found very promising and can be used in real application systems and 503 subjects participated to conduct the experiment.

Investigating a general service retrial queue with damaging and licensed units: an application in local area networks

The idea behind queueing modeling of the proposed work originates from its applicability in various real life service systems. The performance indices obtained for the proposed model can be used to enhance the quality-of-service of many computer networks (LANs, etc.), Internet mail servers and radio communication. In these systems, packets or emails or calls are transmitted from various sources to its respective destination. During this transmission, it may be possible that a packet or an email or a call may be lost due to the effect of an unwanted unit such as ‘virus’ in computer system or ‘collision of packets’ in LANs and others. These unwanted units which force the service provider to collapse immediately are referred as ‘damaging unit’ in terms of queueing theory. This collapsed service provider is then sent for becoming renewed again by the technician present in the system, who makes it perfect after renewed process and the service provider resumes its service. The positive units (which are normal units) and the damaging units arrive to the system in Poisson fashion. We consider that the service provider furnish initial stage of compulsory service referred as ‘first stage service’ to each incoming unit while it furnish non-compulsory services (up to the number l) referred as ‘second stage service’ to only those who urge for the same. On the finish of each service or renewed process, the service provider may abandon the service system and go for break referred as vacation with a random vacation time distribution. Moreover, stochastic decomposition laws have been demonstrated for this proposed model. In addition, numerical experiments and sensitivity analysis are also carried out.

Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution

The large spread of Distributed Energy Resources (DERs) and the related cyber-security issues introduce the need for monitoring. The proposed work focuses on an anomaly detection strategy based on the physical behavior of the industrial process. The algorithm extracts some measures of the physical parameters of the system and processes them with a neural network architecture called autoencoder in order to build a classifier making decisions about the behavior of the system and detecting possible cyber-attacks or faults. The results are quite promising for a practical application in real systems.

Probabilistic wind power forecasting based on spiking neural network

Accurate and reliable quantification of uncertainty in wind power forecasting is critical to the economic operation and real time control of the electric power and energy system. To this end, this paper proposes a novel direct method for probabilistic wind power forecasting based on spiking neural network. In this method, a new forecasting framework is firstly formulated to simultaneously calculate the coverage probability and sharpness with associated confidence levels. Then, group search optimizer is introduced and re-designed to optimize the parameters of the forecasting framework and directly generate the prediction intervals, so that the prediction reliability and stability are ensured. The main advantage of the proposed probabilistic forecasting method is that it does not involve any distribution assumption of the prediction errors required by most existing forecasting methods. The wind power datasets from real wind farms in Belgium and China are used in the case studies. Traditional back-propagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM) are selected as the benchmarking algorithms. The simulation results show that the average coverage of the proposed method is improved by 72.0%, 54.9% and 51.3% respectively, when compared to BPNN, SVM and ELM. The improvement rates of sharpness index are 43.1%, 28.1% and 21.0%, respectively. These statistical results show that the proposed method outperforms BPNN, SVM and ELM in terms of forecasting accuracy, demonstrating that this method has high practical applications in real power systems.

Developing an online hate classifier for multiple social media platforms

The proliferation of social media enables people to express their opinions widely online. However, at the same time, this has resulted in the emergence of conflict and hate, making online environments uninviting for users. Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. To address this research gap, we collect a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labeled as non-hateful and the remaining 20% labeled as hateful. We then experiment with several classification algorithms (Logistic Regression, Naïve Bayes, Support Vector Machines, XGBoost, and Neural Networks) and feature representations (Bag-of-Words, TF-IDF, Word2Vec, BERT, and their combination). While all the models significantly outperform the keyword-based baseline classifier, XGBoost using all features performs the best (F1 = 0.92). Feature importance analysis indicates that BERT features are the most impactful for the predictions. Findings support the generalizability of the best model, as the platform-specific results from Twitter and Wikipedia are comparable to their respective source papers. We make our code publicly available for application in real software systems as well as for further development by online hate researchers.

An application study case tradeoff between throughput and latency on fog-cloud cooperation

Nowadays, several research works are targeting fog-cloud environments cooperation in the organisations. Examples of well-known challenges are workload offloading, shared services, power consumption, security and communication delays. The communication subject is an essential metric to be observed in this cooperation. Bandwidth, throughput and latency are important parameters that affect the fog-cloud cooperation and are frequently misunderstood. In this paper, it is presented a research work, in which we took in consideration a real information system (IS) application which could execute under a cooperation of a fog-cloud configuration. Therefore, we conceived a case study to investigate specially the tradeoff between the throughput and latency parameters and provide more accurate idea of an application requirements in a fog-cloud cooperation scenario.

A New Probabilistic Output Constrained Optimization Extreme Learning Machine

In near decades machine learning approaches have received overwhelming attention from many researchers for solving problems that cannot be ironed out by traditional approaches. However, most of these approaches produces output that is not equivalent to the probability estimates of how credible and reliable the output can be for each prediction. One widely utilized, highly accorded for generalized performance but non-probabilistic machine learning algorithm is the Extreme Learning Machine (ELM). As with other classification systems, ELM generates outputs that cannot be treated as probabilities. Current literature shows approaches attempt to assimilate probabilistic concept in ELM however their outputs are not equivalent to probabilities. Furthermore, these methods invoke two-stage post processing procedures with iterative learning procedures which are against the salient features of ELM that highlight no iterative operations involved in learning. Hence, we want to probe in this paper the ability of ELM to produce probabilistic output from the original architecture of ELM itself while preserving the merits of ELM without the need for a post processing two-stage procedures to convert the output to probability and eliminates iterative learning to compute output weights. Two methodologies of unified probabilistic ELM framework are presented, i.e., Probabilistic Output Extreme Learning Machine (PO-ELM) and Constrained Optimization Posterior Probabilistic Outputs based Extreme Learning Machine (CPP-POELM). The proposed models are evaluated empirically on several benchmark datasets as well as real world power system applications to demonstrate its validity and efficacy in handling pattern classification problems as well as decision making process.

Development of high-speed gripper with closing and releasing capability

There are many recent studies on hyper-human technology like high speed grasping. An existing systems uses the release energy of a spring for high-speed grasping with an arm action with 100G. However, this gripper did not have the ability to release the object. To improve the feasibility of the system for real applications, both grasping and releasing functions are required. In this paper, we propose a prototype high-speed gripper which has the ability to both grasp and release. The conventional system used a spring to achieve high speed gripping. The grasping force decreases as the distance up to the object increases. In contrast, the gripper proposed here uses an air cylinder to open and close the gripper, so that the grasping force does not decrease as the distance increases. Preliminary experiments are conducted and the effectiveness of the proposed mechanism is evaluated.

Sliding Mode Control with Chattering Attenuation and Hardware Constraints in Spacecraft Applications

Many practical issues should be considered when synthesizing a control system for real applications. In this paper the main objective is the evaluation of the performance of a Sliding Model Controller (SMC) for spacecraft applications, in which implementation issues are included. The key features of the proposed practical design are: (i) chattering attenuation, in which an hyperbolic tangent is considered, and (ii) hardware constraints, in which a reduced SMC update frequency and saturations on the actuation system are included. A comparison between a first order and a super-twisting SMC is performed, including disturbances on the mathematical model. Moreover, computational effort and error accuracy are evaluated for both the strategies, showing the performance of the proposed implementation solutions.

Comparative analysis of algorithms for the implementation of the inverse kinematics problem for skeletal animation

The comparative analysis methods for solving inverse kinematics problem was carried out and their implementations were demonstrated in developed software. The initially considered problem in robotics has found its application in modern computer animation, in particular animation of computer games. A heuristic approach to solve this problem was considered and its effectiveness was proved in comparison with classical solution using optimization methods. Based on requirements of real application systems, modifications of the most efficient algorithms have been proposed with the aim of improving them and supporting additional physically justified restrictions. The algorithms Cyclic Coordinate Descent, Triangulation and FABRIC were considered in context of their use in real time. The efficiency and speed of the considered algorithms were analyzed in different conditions and with various configurations, as well as visual differences and subjective naturalness of resulting animations, depending on use cases like animation of animals and people limbs, mechanisms or physical objects. The developed software provides interface for object animation based on skeletal animation using SVG format and various methods of inverse kinematics. The software was created using modern web technologies and allows to create complex skeletons of objects, assign visual representation to chains, sections and whole skeleton, and in various ways describe trajectories of end-effectors movements. This software and mathematical background may be used by animation developers, web developers, for educational purposes, for modeling and motion planning of mechanical systems and robots, etc.

THE ANALYTICAL DESCRIPTION OF FINAL PROBABILITIES FOR STATES OF QUEUING SYSTEMS WITH INPUT FLOW OF GROUPS OF REQUIREMENTS

Context. The management of many economic and other “service” systems of random flows of “requirements” is based on the prediction of their efficiency, based on an estimate of the system states probability distribution. In a number of important practical cases, the input flow may have random composition groups of requirements, which determined the applicability of linear algebra numerical methods for searching probabilities, and also made it difficult to build queuing systems that are effective in a range of conditions and made it impossible to obtain probability estimates for systems with an infinite number of places to wait for service. The objects of the study are Markov models of three types of queuing systems: with refusals, with a limited and with an unlimited number of places to wait in the conditions of the input flow of a random composition groups of requirements. Objective. The goal of the work is to obtain an analytical description of the final state probabilities which are necessary to predict the values of efficiency indicators for three types of Markov multichannel queuing systems: with refusals, with a limited and with an unlimited number of places to wait in the conditions of the input flow of random composition groups of requirements. Method. In the general case, the probabilities of states in queuing systems with input flow random groups of requirements are described by Kolmogorov differential equations. The Kolmogorov equations, in the stationary state of the queuing system, are transformed into a linearly dependent homogeneous system of algebraic equations. The final probabilities of the states of a queuing system can be found by numerically solving a system of equations using methods well known in linear algebra: complete exclusion, the inverse matrix, and the matrix method of Ramaswami [3], [38], which takes into account the repeating block structure of the system of equations matrix. The infinite number of unpredictable combinations for the set of numerical values of the considered queuing systems parameters makes it difficult to control the operation of such systems and to build systems that are effective in a range of conditions. In queuing systems with an unlimited number of places to wait, the number of equations becomes infinite, and numerical methods become unsuitable for final probabilities searching and for solving problems of analysis, synthesis and control of queuing systems. Analytical expressions for the final probabilities of queuing systems are obtained by equivalent transformations of homogeneous systems of algebraic equations in the general case of each type of queuing system mentioned above. Results. The obtained analytical expressions for the final probabilities of the queuing systems states for three noted system types are not previously known and therefore required verification of their correctness. Such a check was performed by the way of degenerate the flow of random groups of requirements in the input of the system to the simplest flow of requirements. As a result of verification, analytical expressions for the final probabilities of the considered systems states were automatically transformed into the corresponding well-known models of queuing systems with the simplest input flow of requirements. This effect allows us to consider the well-known models of queuing systems of the simplest input requirements flow – to be a particular case of the obtained models of queuing systems with an input flow of groups of requirements. Conclusions. To further verify the correctness of the results and to assess the degree of influence of requirements random number in groups of input flow onto the system efficiency, a numerical example is given for the critical conditions of a constant intensity of requirements flow equal to the total performance of the system’s service channels. In this case, only the average number of requirements in groups changed. The results of the numerical experiment testify in favor of the correctness of the obtained analytical expressions for the final probabilities and in favor of the possibility of their practical application in real queuing systems when solving problems of forecasting efficiency, as well as analyzing and synthesizing the parameters of real queuing systems.

Echo state network based ensemble approach for wind power forecasting

Accurate and reliable wind power forecasting is of great significance for the economic and safe operation of electric power and energy systems. However, due to the negative factors such as inaccurate numerical weather prediction and frequent ramping events, previous wind power prediction methods are difficult to meet actual needs for practical applications. To this end, this paper originally proposes a new wind power prediction method with high accuracy based on echo state network. This method is a hybrid of wavelet transform, echo state network and ensemble technique. Wavelet transform is used to decompose raw wind power time series data into different frequencies with better outliers and behaviors. Then, we adopt the echo state network to automatically learn the nonlinear mapping relationship between input and output in each frequency. Later, ensemble technique is applied to deal with the model misspecification and data noise problems, which are common in wind power prediction, thereby reducing the uncertainty of wind power forecasting and improving prediction accuracy. Finally, we use wind power data from real wind farms in Belgium and China to verify the feasibility and effectiveness of the proposed method. The simulation results show that the proposed method is superior to other benchmark algorithms in prediction accuracy, which indicates that the method has high potentials for practical application in real electric power and energy systems.

Optimal design of CDM controller to frequency control of a realistic power system equipped with storage devices using grasshopper optimization algorithm

This paper presents a new robust load frequency control (LFC) technique based on an optimal design of the coefficient diagram method (CDM) in a three area thermal power system equipped with redox flow batteries (RFB). In order to emphasize on a realistic power system and obtain an accurate insight, important nonlinearities due to generation rate constraint (GRC), governor dead band (GDB) and time delay (TD) were considered. The innovation of the proposed controller in this paper is the use of a hybrid intelligent combination of a decentralized CDM technique and optimization throughout its algebraic equations. In addition, a new algorithm namely grasshopper optimization algorithm (GOA) was used to find the key parameters of the proposed controller in solving the LFC problem for the first time. Furthermore, this study applied a powerful modified objective function by considering the integral of time multiplied squared error (ITSE) criteria for both controller input signal (ACE), to minimize the area control error and output signal (Δu) to reduce the size of the actuator, settling time (Ts) to have a faster response and a function to increase the minimum damping ratio (MDR) among all eigen values. To demonstrate the effectiveness of the proposed scheme, the studied simulated power system was tested through different cases including a large step and sinusoidal load perturbations and wide uncertainty in dynamic parameters of a nonlinear power system. Comparative results have revealed the superiority of the optimal CDM technique, especially when it is equipped with RFB. In addition to graphical results, by taking into account the MDR of different control strategies, the preference of the proposed controller has become more validated. This newly developed strategy leads to a flexible and accurate controller with a powerful mathematical back up which can successfully cope with GRC, GDB and TD nonlinearities in perturbed uncertain power systems and provide fast, stable and robust dynamic responses. Thus, the proposed control strategy can be constructive and successfully applied to real world power system application.

Data-Driven Condition Monitoring of Data Acquisition for Consumers’ Transformers in Actual Distribution Systems Using t-Statistics

Consumers’ transformers play an important role in power systems, and they are essential for operation reliability and commercial benefits. In the past, maintenance personnel had to spend plenty of time on examining consumers’ transformers one by one. Nowadays, with the wide deployment of power user electric energy data acquire system (PUEEDAS), informative metering data are becoming available, which can be utilized for further condition monitoring. Thus, this paper proposes a data-driven abnormal condition monitoring algorithm of data acquisition for consumers’ transformers, which could timely send abnormal condition alerts to operators and maintenance personnel. In the proposed algorithm, Spearman's rank correlation coefficient is utilized to show the degree of correlation among phase currents, and its t -Statistics is used to determine whether abnormal condition of data acquisition exists based on the hypothesis testing. Finally, actual acquisition data from Zhejiang power system in China are employed to validate the effectiveness of the proposed algorithm, and to analyze the characteristics of normal and abnormal conditions, respectively. Sensitive analyses on different significant levels and sampling rates are performed for considering its impact on monitoring results; the application in real power systems is also given to demonstrate the practicality of the proposed algorithm.

Dissipativity based fault detection for 2D Markov jump systems with asynchronous modes

The problem of fault detection for two-dimensional (2D) Markov jump systems characterized in the form of Roesser model is considered in this paper. An asynchronous fault detection filter is designed to produce a residual signal. The fault detection filter changes from one mode to another asynchronously with the plant’s transitions. More specifically, the filter’s mode transitions depend on the plant’s mode through some conditional probabilities. Moreover, the transition probabilities of the plant and the conditional probabilities are only partially accessible, which appears more practical in real application systems. Under such a framework, sufficient conditions are developed to ensure the asymptotic mean square stability and (Q, R, S)-μ-dissipativity of the overall fault detection system. The parameters in the fault detection filter are given in terms of solutions of an optimization problem. Finally, some simulations are carried out to demonstrate the validity of the presented design techniques.

Sparse Recovery and Dictionary Learning to Identify the Nonlinear Dynamical Systems: One Step Toward Finding Bifurcation Points in Real Systems

Modeling real dynamical systems is an important challenge in many areas of science. Extracting governing equations of systems from their time-series is a possible solution for such a challenge. In this paper, we use the sparse recovery and dictionary learning to extract governing equations of a system with parametric basis functions. In this algorithm, the assumption of sparsity in the functions of dynamical equations is used. The proposed algorithm is applied to different types of discrete and continuous nonlinear dynamical systems to show the generalization ability of this method. On the other hand, transition from one dynamical regime to another is an important concept in studying real world complex systems like biological and climate systems. Lyapunov exponent is an early warning index. It can predict bifurcation points in dynamical systems. Computation of Lyapunov exponent is a major challenge in its application in real systems, since it needs long time data to be accurate. In this paper, we use the predicted governing equation to generate long time-series, which is needed for Lyapunov exponent calculation. So the proposed method can help us to predict bifurcation points by accurate calculation of Lyapunov exponents.

Self-excited and hidden attractors in a novel chaotic system with complicated multistability

In this paper, a new 3D chaotic system with trigonometric function term as a nonlinear controller is proposed. Depending on the nonlinear controller and the value of the parameters, the proposed system exhibits self-excited attractor with an unstable equilibrium, and hidden attractor with no equilibrium or a stable equilibrium. In addition, the unusual and striking dynamic behavior of the coexistence of self-excited chaotic with periodic attractors, two self-excited chaotic attractors with periodic attractor, three periodic attractors, hidden chaotic with point attractors, two hidden chaotic attractors, and four hidden chaotic attractors are explored by selecting appropriate initial values. Consequently, the proposed system has high sensitivity to its initial values and parameters, hence it can be applied in chaos-based cryptographic applications. Thus, the non-periodicity of coexisting attractors of the system is investigated through Lyapunov exponents and Sample Entropy. To demonstrate the performance of the system in real applications, we construct a pseudo-random number generator (PRNG) based on the hidden attractor case. The randomness test results show that the generated PRNG pass all the statistical tests.

Development of a procedure for the synthesis of a micro gas turbine engine neural controller with account for fuel consumption constraints

Neural networks are often used to model dynamic processes in objects and to synthesize their control systems. However, their application in real systems is now rather limited due to insufficient research into the process of creating control systems. The issues of creating neural network control systems for gas turbine engines, taking into account both their nonlinear dynamics and the fuel consumption constraints depending on the engine operating mode, remain practically unexplored. To take into account the fuel consumption constraints, a method was developed for modifying the misalignment between the actual and target RPM values during the training of the neural controller. The resulting neural controller is characterized by implicit fuel consumption constraints and non-linear dynamics of the engine itself. The developed method for modifying the neural network training error allows one to synthesize a nonlinear control system, taking into account the requirements for limitations in the automatic mode.

Low temperature synthesis and structural, dielectric and optical studies of Bi1−xGdxFeO3 (x = 0, 0.10, 0.11, 0.12, 0.13, 0.14, 0.15) nanoceramics

Gd-substituted BiFeO3 (BFO) multiferroic nanoceramics with the compositional formula Bi1-xGdxFeO3 (x = 0, 0.10, 0.11, 0.12, 0.13, 0.14, 0.15) were prepared by low temperature sol gel method. Phase identification of all the samples were carried out using XRD analysis. FTIR analyses showed the presence of stretching and bending vibrations of various bonds present in all the samples. Dielectric properties such as dielectric constant and dielectric loss were measured as a function of frequency and Gd concentration, wherein the enhanced dielectric properties were observed with the addition of Gd dopant. The optical absorbance spectra of all the samples were measured and the energy band gaps (Eg) were obtained from Tauc plot. Explanation of the observed results was discussed and various possible applications in real multiferroic systems were highlighted.