Power system oscillation is a serious threat to the stability of multimachine power systems. The coordinated control of power system stabilizers (PSS) and thyristor-controlled series compensation (TCSC) damping controllers is a commonly used technique to provide the required damping over different modes of growing oscillations. However, their coordinated design is a complex multimodal optimization problem that is very hard to solve using traditional tuning techniques. In addition, several limitations of traditionally used techniques prevent the optimum design of coordinated controllers. In this paper, an alternate technique for robust damping over oscillation is presented using backtracking search algorithm (BSA). A 5-area 16-machine benchmark power system is considered to evaluate the design efficiency. The complete design process is conducted in a linear time-invariant (LTI) model of a power system. It includes the design formulation into a multi-objective function from the system eigenvalues. Later on, nonlinear time-domain simulations are used to compare the damping performances for different local and inter-area modes of power system oscillations. The performance of the BSA technique is compared against that of the popular particle swarm optimization (PSO) for coordinated design efficiency. Damping performances using different design techniques are compared in term of settling time and overshoot of oscillations. The results obtained verify that the BSA-based design improves the system stability significantly. The stability of the multimachine power system is improved by up to 74.47% and 79.93% for an inter-area mode and a local mode of oscillation, respectively. Thus, the proposed technique for coordinated design has great potential to improve power system stability and to maintain its secure operation.
This paper presents the concept of Simultaneous Localization and Multi-Mapping (SLAMM). It is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor's malfunction; making it suitable for real-world applications. It works with single or multiple robots. In a single robot scenario the algorithm generates a new map at the time of tracking failure, and later it merges maps at the event of loop closure. Similarly, maps generated from multiple robots are merged without prior knowledge of their relative poses; which makes this algorithm flexible. The system works in real time at frame-rate speed. The proposed approach was tested on the KITTI and TUM RGB-D public datasets and it showed superior results compared to the state-of-the-arts in calibrated visual monocular keyframe-based SLAM. The mean tracking time is around 22 milliseconds. The initialization is twice as fast as it is in ORB-SLAM, and the retrieved map can reach up to 90 percent more in terms of information preservation depending on tracking loss and loop closure events. For the benefit of the community, the source code along with a framework to be run with Bebop drone are made available at https://github.com/hdaoud/ORBSLAMM.
Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.
In this paper, the fully implicit 2-point block backward differentiation formula and diagonally implicit 2-point block
backward differentiation formula were developed under the interpretation of generalized differentiability concept for
solving first order fuzzy differential equations. Some fuzzy initial value problems were tested in order to demonstrate the
performance of the developed methods. The approximated solutions for both methods were in good agreement with the
exact solutions. The numerical results showed that the diagonally implicit method outperforms the fully implicit method
in term of accuracy.
Classification is one of the most hourly encountered problems in real world. Neural networks have
emerged as one of the tools that can handle the classification problem. Feed-Forward Neural Networks
(FFNN's) have been widely applied in many different fields as a classification tool. Designing an efficient
FFNN structure with the optimum number of hidden layers and minimum number of layer's neurons for
a given specific application or dataset, is an open research problem and more challenging depend on
the input data. The random selections of hidden layers and neurons may cause the problem of either
under fitting or over fitting. Over fitting arises because the network matches the data so closely as to
lose its generalization ability over the test data. In this research, the classification performance using
the Mean Square Error (MSE) of Feed-Forward Neural Network (FFNN) with back-propagation algorithm
with respect to the different number of hidden layers and hidden neurons is computed and analyzed to
find out the optimum number of hidden layers and minimum number of layer's neurons to help the
existing classification concepts by MATLAB version 13a. By this process, firstly the random data has
been generated using an suitable matlab function to prepare the training data as the input and target
vectors as the testing data for the classification purposes of FFNN. The generated input data is passed
on to the output layer through the hidden layers which process these data. From this analysis, it is find
out from the mean square error comparison graphs and regression plots that for getting the best
performance form this network, it is better to use the high number of hidden layers and more neurons in
the hidden layers in the network during designing its classifier but so more neurons in the hidden layers
and the high number of hidden layers in the network makes it complex and takes more time to execute.
So as the result it is suggested that three hidden layers and 26 hidden neurons in each hidden layers
are better for designing the classifier of this network for this type of input data features.
The present paper deals with the novel approach for clustering using the image feature of stabilization diagram for automated operational modal analysis in parametric model which is stochastic subspace identification (SSI)-COV. The evolution of automated operational modal analysis (OMA) is not an easy task, since traditional methods of modal analysis require a large amount of intervention by an expert user. The stabilization diagram and clustering tools are introduced to autonomously distinguish physical poles from noise (spurious) poles which can neglect any user interaction. However, the existing clustering algorithms require at least one user-defined parameter, the maximum within-cluster distance between representations of the same physical mode from different system orders and the supplementary adaptive approaches have to be employed to optimize the selection of cluster validation criteria which will lead to high demanding computational effort. The developed image clustering process is based on the input image of the stabilization diagram that has been generated and displayed separately into a certain interval frequency. and standardized image features in MATLAB was applied to extract the image features of each generated image of stabilisation diagrams. Then, the generated image feature extraction of stabilization diagrams was used to plot image clustering diagram and fixed defined threshold was set for the physical modes classification. The application of image clustering has proven to provide a reliable output results which can effectively identify physical modes in stabilization diagrams using image feature extraction even for closely spaced modes without the need of any calibration or user-defined parameter at start up and any supplementary adaptive approach for cluster validation criteria.
Maximum k Satisfiability logical rule (MAX-kSAT) is a language that bridges real life application to neural network optimization. MAX-kSAT is an interesting paradigm because the outcome of this logical rule is always negative/false. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when the logical rule is embedded in HNN. Increasing the storage capacity during the learning phase of HNN has been a challenging problem for most neural network researchers. Development of Metaheuristics algorithms has been crucial in optimizing the learning phase of Neural Network. The most celebrated metaheuristics model is Genetic Algorithm (GA). GA consists of several important operators that emphasize on solution improvement. Although GA has been reported to optimize logic programming in HNN, the learning complexity increases as the number of clauses increases. GA is more likely to be trapped in suboptimal fitness as the number of clauses increases. In this paper, metaheuristic algorithm namely Artificial Bee Colony (ABC) were proposed in learning MAX-kSAT programming. ABC is swarm-based metaheuristics that capitalized the capability of Employed Bee, Onlooker Bee, and Scout Bee. To this end, all the learning models were tested in a new restricted learning environment. Experimental results obtained from the computer simulation demonstrate the effectiveness of ABC in modelling MAX-kSAT.
Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation. Although not all computing functions can translate to the SC domain, several useful function blocks related to the CNN algorithm had been proposed and tested by researchers. An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency. This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then compare the advantages and disadvantages amongst different SC methods. Finally, we conclude the overview of SC in CNN and make suggestions for widespread implementation.
This paper presents parameters analysis for the estimated modal damping ratio using a new version of the automated enhanced frequency domain decomposition (AEFDD). The purpose of this study is to provide a better choice of a maximum number of points of time segments and modal assurance criterion (MAC) index number regarding to the variable level of system damping (low and high damped structure) and degree of freedom of the system. According current literature, frequency domain (FD) methods seem to have the problem with providing a correct identification of the modal damping ratio, since the correct estimate of modal damping is still an open problem and often leads to biased estimates. This technique is capable of providing consistent modal parameters estimation, particularly for modal frequencies and mode shapes. As a necessary fundamental condition, the algorithm has been assessed first from computed numerical responses according to random white noise, acting on different shear-type frame structures and corrupted with noise. Results indicate that reducing the value of natural frequencies and modal damping ratios of the modes under analysis demands longer time segments and a high value of the maximum number of points for adequate information on the decaying correlation functions when computing a modal damping ratio. In addition, the results also prove that the MAC index does not significantly affect the results for the low damped system. However, the use of a high MAC index value for the high damped system significantly introduces large error bound and it becomes worse, particularly for the higher modes, as the standard deviation of percentage error increases gradually. Furthermore, the use of a MAC index for a high number of points of time segments significantly increases the standard deviation of the percentage error.
Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.
Linear array of permutations is hard to be factorised. However, by using a starter set, the process of listing the permutations becomes easy. Once the starter sets are obtained, the circular and reverse of circular operations are easily employed to produce distinct permutations from each starter set. However, a problem arises when the equivalence starter sets generate similar permutations and, therefore, willneed to be discarded. In this paper, a new recursive strategy is proposed to generate starter sets that will not incur equivalence by circular operation. Computational advantages are presented that compare the results obtained by the new algorithm with those obtained using two other existing methods. The result indicates that the new algorithm is faster than the other two in time execution.
The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.
The fabrication tolerance of a short and compact low refractive index grating waveguide polarisation splitter based on the principle of resonant tunnelling was analyzed in this study. The design utilised two grating waveguides with an intermediate conventional waveguide layer. The design and optimisation were conducted using the quasi 2-D effective index solver with global search algorithm. An optimum device operating at 1.55 μm wavelength was obtained at a length of 340 μm. The splitting ratios were calculated to be 36 dB and 15 dB, and the overall device transmission efficiencies, after considering the three-dimensional waveguide leakage loss, were estimated at 88% and 83% for tranverse magnetic and tranverse electric polarisation, respectively.
In this paper, an improved method of reducing ambient noise in speech signals is introduced. The proposed noise canceller was developed using a computationally efficient (DFT) filter bank to decompose input signals into sub-bands. The filter bank was based on a prototype filter optimized for minimum output distortion. A variable step-size version of the (LMS) filter was used to reduce the noise in individual branches. The subband noise canceller was aimed to overcome problems associated with the use of the conventional least mean square (LMS) adaptive algorithm in noise cancellation setups. Mean square error convergence was used as a measure of performance under white and ambient interferences. Compared to conventional as well as recently developed techniques, fast initial convergence and better noise cancellation performances were obtained under actual speech and ambient noise.
The nonlinear conjugate gradient (CG) methods have widely been used in solving unconstrained optimization problems. They are well-suited for large-scale optimization problems due to their low memory requirements and least computational costs. In this paper, a new diagonal preconditioned conjugate gradient (PRECG) algorithm is designed, and this is motivated by the fact that a pre-conditioner can greatly enhance the performance of the CG method. Under mild conditions, it is shown that the algorithm is globally convergent for strongly convex functions. Numerical results are presented to show that the new diagonal PRECG method works better than the standard CG method.
Elliptic curve cryptosystems (ECC) provides better security for each bit key utilized compared to the RSA cryptosystem. For this reason, it is projected to have more practical usage than the RSA. In ECC, scalar multiplication (or point multiplication) is the dominant operation, namely, computing nP from a point P on an elliptic curve, where n is an integer defined as the point resulting from adding P + P + ... + P, n times. However, for practical uses, it is very important to improve the efficiency of the scalar multiplication. Solinas (1997) proposes that the τ-adic Non-Adjacent Form (τ-NAF) is one of the most efficient algorithms used to compute scalar multiplications on Anomalous Binary curves. In this paper, we give a new property (i.e., Theorem 1.2) of τ-NAF(n) representation for every length, l. This is useful for evaluating the maximum and minimum norms occurring among all length-l elements of Z(τ). We also propose a new cryptographic method by using randomization of a multiplier n to nr an element of Z(τ). It is based on τ-NAF. We focused on estimating the length of RTNAF(nr) expansion by using a new method.
Nowadays, the applications of tracking moving object are commonly used in various
areas especially in computer vision applications. There are many tracking algorithms
have been introduced and they are divided into three groups which are generative
trackers, discriminative trackers and hybrid trackers. One of the methods is TrackingLearning-Detection
(TLD) framework which is an example of the hybrid trackers where
combination between the generative trackers and the discriminative trackers occur. In
TLD, the detector consists of three stages which are patch variance, ensemble classifier
and KNearest Neighbor classifier. In the second stage, the ensemble classifier depends
on simple pixel comparison hence, it is likely fail to offer a better generalization of the
appearances of the target object in the detection process. In this paper, OnlineSequential
Extreme Learning Machine (OS-ELM) was used to replace the ensemble
classifier in the TLD framework. Besides that, different types of Haar-like features were
used for the feature extraction process instead of using raw pixel value as the features.
The objectives of this study are to improve the classifier in the second stage of detector
in TLD framework by using Haar-like features as an input to the classifier and to get a
more generalized detector in TLD framework by using OS-ELM based detector. The
results showed that the proposed method performs better in Pedestrian 1 in terms of
F-measure and also offers good performance in terms of Precision in four out of six
videos.
Real life phenomena found in various fields such as engineering, physics,
biology and communication theory can be modeled as nonlinear higher order ordinary
differential equations, particularly the Duffing oscillator. Analytical solutions for these
differential equations can be time consuming whereas, conventional numerical solutions
may lack accuracy. This research propose a block multistep method integrated with a
variable order step size (VOS) algorithm for solving these Duffing oscillators directly.
The proposed VOS Block method provides an alternative numerical solution by reducing
computational cost (time) but without loss of accuracy. Numerical simulations
are compared with known exact solutions for proof of accuracy and against current
numerical methods for proof of efficiency (steps taken).
In this paper, we study the numerical method for solving second order Fuzzy
Differential Equations (FDEs) using Block Backward Differential Formulas (BBDF)
under generalized concept of higher-order fuzzy differentiability. Implementation of
the method using Newton iteration is discussed. Numerical results obtained by BBDF
are presented and compared with Backward Differential Formulas (BDF) and exact
solutions. Several numerical examples are provided to illustrate our methods.
A new method to construct the distinct Hamiltonian circuits in complete
graphs is called Half Butterfly Method. The Half Butterfly Method used the concept
of isomorphism in developing the distinct Hamiltonian circuits. Thus some theoretical
works are presented throughout developing this method.