Sequential Machine Research Articles

High-Performance Parallel Implementation of Genetic Algorithm on FPGA

Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposes in this paper is able to work with more variable from some adjustments on hardware architecture.

Dynamic reconstruction of chaotic system based on exponential weighted online sequential extreme learning machine with kernel

For the dynamic reconstruction of the chaotic dynamical system, a method of identifying an exponential weighted online sequential extreme learning machine with kernel(EW-KOSELM) is proposed. The kernel recursive least square (KRLS) algorithm is directly extended to an online sequential ELM framework, and weakens the effect of old data by introducing a forgetting factor. Meanwhile, the proposed algorithm can deal with the ever-increasing computational difficulties inherent in online kernel learning algorithms based on the ‘fixed-budget’ memory technique. The employed EW-KOSELM identification method is firstly applied to the numerical example of Duffing-Ueda oscillator for chaotic dynamical system based on simulated data, the qualitative and quantitative analysis for various validation tests of the dynamical properties of the original system as well as the identification model are carried out. A set of qualitative validation criteria is implemented by comparing chaotic attractors i.e. embedding trajectories, computing the corresponding Poincare mapping, plotting the bifurcation diagram, and plotting the steady-state trajectory i.e. the limit cycle between the original system and the identification model. Simultaneously, the quantitative validation criterion which includes computing the largest positive Lyapunov exponent and the correlation dimension of the chaotic attractors is also calculated to measure the closeness i.e. the approximation error between the original system and the identification model. The employed method is further applied to a practical implementation example of Chua's circuit based on the experimental data which are generated by sampling and recording the measured voltage across a capacitor, the inductor current from the double-scroll attractor, the measured voltage across a capacitor from the Chua's spiral attractor and an experimental time series from a chaotic circuit. The digital filtering technique is then used as a preprocessing approach, on the basis of wavelet denoising the measured data with lower signal-noise ratio (SNR) which can produce the double-scroll attractor or the spiral attractor, the reconstruction attractor of the identification model is compared with the reconstruction attractor from the experimental data for original system. The above experimental results confirm that the EW-FB-KOSELM identification method has a better performance of dynamic reconstruction, which can produce an accurate nonlinear model of process exhibiting chaotic dynamics. The identification model is dynamically equivalent or system approximation to the original system.

Enhanced Online Sequential Parallel Extreme Learning Machine and its Application in Remaining Useful Life Prediction of Integrated Modular Avionics

Integrated modular avionics is one of the most advanced systems which has been widely applied in modern aircraft. The performance of integrated modular avionics deeply impacts flight mission. Remaining useful life prediction is the critical manner which can efficiently improve the safety and reliability of aircraft. Since integrated modular avionics is a real-time system, the prediction algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced online sequential method is proposed to predict the remaining useful life of integrated modular avionics. Firstly, this paper proposes an online sequential network which is based on the architecture of parallel layer network. Secondly, to enhance the learning capability, this paper adopts the extreme learning machine denosing autoencoder to determine the input weights of the network. Thirdly, an adaptive forgetting factor is added to further improve the performance of the proposed method. The effectiveness and the superiority of the proposed method are verified in comparison with three online sequential algorithms on the standard datasets. Finally, a degradation model is built to depict the deteriorated process of integrated modular avionics. The prediction results confirms that the proposed method can effectively address remaining useful life of integrated modular avionics.

Fault tolerant control for a class of interconnected asynchronous sequential machines

This paper considers fault tolerant control for parallel interconnected asynchronous sequential machines (ASMs) governed by a single corrective controller with output feedback. The control objective is to diagnose unauthorized state transitions and to recover the normal input/output behavior of the closed-loop system in an asynchronous mechanism. The existence condition and design algorithm for a fault tolerant controller is addressed in the framework of corrective control. The proposed scheme is efficient in that it does not require complete modeling of parallel composition nor output bursts in the feedback channel. An illustrative example is provided to demonstrate the procedure of controller synthesis.

Exact fault recovery for asynchronous sequential machines with output bursts

This paper presents an enhanced fault tolerant control strategy for a class of input/output asynchronous sequential machines (ASMs) in which output feedback takes the form of bursts. We design a corrective controller that counteracts any unauthorized state transition occurring to the ASM. As the controlled machine is steered on a feedback path, uncertainty about the goal state is reduced by the information that output bursts provide. Hence the use of output bursts in the correction procedure allows the controller to conduct more refined fault recovery than the case of accessing unit output characters as feedback. We present the existence condition and design procedure for a controller that achieves exact fault recovery, and provide an illustrative example to demonstrate the procedure of controller synthesis.

Robust Controllability of Switched Asynchronous Sequential Machines

This paper investigates robust controllability of switched asynchronous sequential machines (ASMs) with permanent faults. A switched ASM can change its mode through a number of submachines in an asynchronous mechanism. The main objective is to develop a condition for the corrective control scheme that makes the closed-loop system robust against permanent faults while matching its stable-state behavior to that of a reference model. The reachability degradation of the switched ASM caused by faults is described in terms of a matrix expression. In the proposed methodology, each submachine serves as a structural redundancy of the switched ASM allowing robust corrective control of the closed-loop system.

Electric-Parameter-Based Inversion of Dynamometer Card Using Hybrid Modeling for Beam Pumping System

Conventional fault diagnosis and production calculation of an oil well can be conducted with the surface dynamometer cards, which are obtained by load sensor installed on the horse head. This method to measure the dynamometer cards is limited by the sensor maintenance and calibration, battery replacement, and safety hazards for staff. As the basic parameter of the oil extraction industry, electric parameters have the advantages of low cost and high efficiency. So the inversions of dynamometer card with electric parameters are attracting more and more attention. In order to solve the problem of insufficient data and consider the real-time performance in the actual oil extraction process, this paper proposes a novel hybrid model which consists of two parts: the mechanism model of polished rod load and the suspension displacement calculated with the space vector equations of motor and a data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) model proposed to correct the output error of the mechanism model, which improves the kernel function selection and makes it real-time. Thus, the highlights of this paper can be summed up in two points: (1) under the circumstance of the bottom dead point detection without sensors, the mechanism modeling of the polished rod load and suspension displacement has been carried out from the perspective of mathematical model of AC motor; (2) a novel data-driven model based on data-dependent kernel online sequential extreme learning machine (DDKOS-ELM) has been proposed to improve the kernel functions selection. The coefficients in the data-dependent kernel function are optimized with improved free search algorithm (IFSA). The proposed hybrid model has been applied to a normal working oil well and the prediction results show better accuracy than the pure data-driven model and mechanism model.

Implementation of Sequential Machine for Encryption of Textual Information

Cryptography is a secured transfer of information between users to avoid leakage of information. Research of techniques for cryptography using public key is done to have a need of great computational power besides being highly complex and time consuming for obtaining the key. Neural networks are found to have an edge over these shortcomings for obtaining the keys. In this paper, n – state sequential machine is used to examine Artificial Neural Network as a toll for cryptography. The results thereby obtained demonstrate the use of artificial neural network for cryptography.

Implementation of Sequential Machine for Encryption of Textual Information

Cryptography is a secured transfer of information between users to avoid leakage of information. Research of techniques for cryptography using public key is done to have a need of great computational power besides being highly complex and time consuming for obtaining the key. Neural networks are found to have an edge over these shortcomings for obtaining the keys. In this paper, n – state sequential machine is used to examine Artificial Neural Network as a toll for cryptography. The results thereby obtained demonstrate the use of artificial neural network for cryptography.

Vibration analysis and identification of breathing cracks in beams subjected to single or multiple moving mass using online sequential extreme learning machine

ABSTRACTIn this paper, a novel approach was presented to vibration analysis and identification of breathing cracks in Timoshenko beam under single or multiple moving mass. Dynamic strain energies (DSEs) and translational accelerations in beam structures under moving mass were used as forward problem and application of an emergent learning algorithm called the online sequential extreme learning machine algorithm as inverse problem to predict crack depths and locations. To demonstrate the potential of the proposed vibration analysis over existing ones, two validation studies have been done. To evaluate the proposed method to identify breathing cracks, two examples, namely, clamped–clamped beam and two span continuous beams have been studied. Also, the effect of the discrepancy in stiffness between the finite-element model and the actual tested dynamic system has been investigated. Another examination has been performed in which moving mass with different speeds are utilized. Also, the effect of multi mass passing through the beam has been studied. The obtained results indicated that the proposed method could identify the breathing cracks existence and severity in the beam under moving mass using DSE and accelerations, which may be noisy or noise free.

Model matching of switched asynchronous sequential machines via matrix approach

ABSTRACTThis paper investigates model matching problem of switched asynchronous sequential machines (ASMs) via semi-tensor product (STP) of matrices. A switched ASM is composed of several input/state ASMs and it cut-overs between submachines in asynchronous mechanism. By using STP method, a switched ASM is depicted as a discrete-time equation form. Under this framework, stable transition matrix and admissible model-input-state transition matrix are defined. Then, two important problems, reachability and optimal control input design of switched ASMs, are discussed by these two new matrices, respectively. Finally, a necessary and sufficient condition for the existence of a controller for model matching is obtained and a practical example on asynchronous error accumulators is provided to show the feasibility of the proposed method.

Fault recovery for cascaded asynchronous sequential machines

In this study, the authors investigate the fault-tolerant control problem of a cascaded asynchronous sequential machine (ASM) in which two ASMs subject to adversarial inputs constitute a cascade composition. The main objective is to develop a corrective controller that can diagnose and tolerate any unauthorised state transition occurring in the cascaded machine. In particular, the authors address fault-tolerant control for two control configurations, one with a state feedback and the other with an output feedback. The existence condition for a controller is found to be different with respect to each feedback availability. The authors apply the proposed control scheme to robust operations of memory scrubbing modules in space-borne computers implemented on field-programmable gate array systems. Experimental results show the practicability of the proposed methodology.

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

The fault diagnosis of rolling element bearings is very important for ensuring the safe operation of rotary machineries. Targeting the nonstationary characteristics of the vibration signals of rolling element bearings, a novel approach based on dual-tree complex wavelet packet transform, improved intrinsic time-scale decomposition, and the online sequential extreme learning machine is proposed in this article for the fault recognition of rolling element bearing. First, the feature extraction method of the measured signal is presented by combining improved intrinsic time-scale decomposition with dual-tree complex wavelet packet transform as preprocessor and two-step screening processes based on the energy ratio, the vibration signal is adaptively decomposed into a set of proper rotation components; second, the matrix formed by different proper rotation components and singular value decomposition is used to obtain singular value as eigenvector; finally, singular values are input to online sequential extreme...

Conditions for Fault Recovery of Interconnected Asynchronous Sequential Machines with State Feedback

Conditions for Fault Recovery of Interconnected Asynchronous Sequential Machines with State Feedback

An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series

Online Value-at-Risk (VaR) analysis in high-dimensional space remains a challenge in the era of big data. In this paper, we propose an online sequential learning non-parametric VaR model called OS-GELM which is an autonomous cognitive system. This model uses a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process and an online sequential extreme learning machine (OS-ELM) to cognitively calculate VaR, which can be used for online risk analysis. The proposed model not only learns the data one-by-one or chunk-by-chunk but also calculates VaR in real time by extending OS-ELM from machine learning to the non-parametric GARCH process. The GARCH process is also extended to one-by-one and chunk-by-chunk mode. In OS-GELM, the parameters of hidden nodes are randomly selected. The output weights are analytically determined based on the sequentially arriving data. In addition, the generalization performance of the OS-GELM model attains a small training error and generates the smallest norm of weights. Experimentally obtained VaRs are compared with those given by GARCH-type models and conventional OS-ELM. The computational results demonstrate that the OS-GELM model obtains more accurate results and is better at forecasting the online VaR. OS-GELM model is an autonomous cognitive system to dynamically calculate Value-at-Risk, which can be used for online financial risk assessment about human being’s behavior. The OS-GELM model can calculate VaR in real time, which can be used as a tool for online risk management. OS-GELM can handle any bounded, non-constant, piecewise-continuous membership function to realize real-time VaR monitoring.

Calculating skeleton matrix of asynchronous sequential machines based on the semi‐tensor product of matrices

In this study, the problem of calculating the skeleton matrix for asynchronous sequential machine (ASMs) is addressed in the framework of the semi-tensor product (STP) of matrices. By applying the STP of matrices and Boolean algebra, a new algebraic expression of ASMs is deduced. By utilising the algebraic expression, the skeleton matrix of ASMs can be easily derived, then the notion of generalised skeleton matrix is further defined for the ASMs with infinite cycles or critical races, and an algorithm of calculating the generalised skeleton matrix is also proposed. For the model matching problem of input/output ASMs, a novel approach is proposed to calculate reachability indictor and generalised fused skeleton matrix. Two examples are presented to illustrate the effectiveness of the proposed results.

A Meteorological Predication Model Based on Storm and Online Sequential Extreme Learning Machine

A Meteorological Predication Model Based on Storm and Online Sequential Extreme Learning Machine

Reverse engineering concurrent UML state machines using black box testing and genetic programming

This paper presents a technique for reverse engineering, a software system generated from a concurrent unified modeling language state machine implementation. In its first step, a primitive sequential finite-state machine (FSM) is deduced from a sequence of outputs emitted from black box tests applied to the systems’ input interface. Next, we provide an algorithmic technique for decomposing the sequential primitive FSM into a set of concurrent (orthogonal) primitive FSMs. Lastly, we show a genetic programming machine learning technique for discovering local variables, actions performed on local and non-binary output variables, and two types of intra-FSM loops, called counting-loops and while-loops.

Matrix approach to model matching of composite asynchronous sequential machines

Composite asynchronous sequential machines (ASMs) are studied by a matrix-based approach in this study. A composite ASM consists of several input/state asynchronous machines in a cascading fashion. By using the semi-tensor product method, the algebraic expression of composite ASMs is presented, based on which reachability of composite ASMs is investigated. Then, a necessary and sufficient condition for the existence of a controller for model matching is obtained and an algorithm to design the corresponding controller is established. Finally, an example is proposed to illustrate the effectiveness of the obtained results.

Release of polyester and cotton fibers from textiles in machine washings.

Microplastics are widely spread in the environment, which along with still increasing production have aroused concern of their impacts on environmental health. The objective of this study is to quantify the number and mass of two most common textile fibers discharged from sequential machine washings to sewers. The number and mass of microfibers released from polyester and cotton textiles in the first wash varied in the range 2.1×105 to 1.3×107 and 0.12 to 0.33% w/w, respectively. Amounts of released microfibers showed a decreasing trend in sequential washes. The annual emission of polyester and cotton microfibers from household washing machines was estimated to be 154,000 (1.0×1014) and 411,000kg (4.9×1014) in Finland (population 5.5×106). Due to the high emission values and sorption capacities, the polyester and cotton microfibers may play an important role in the transport and fate of chemical pollutants in the aquatic environment.