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Open access

George Tambouratzis and Marina Vassiliou

Abstract

The present article describes a novel phrasing model which can be used for segmenting sentences of unconstrained text into syntactically-defined phrases. This model is based on the notion of attraction and repulsion forces between adjacent words. Each of these forces is weighed appropriately by system parameters, the values of which are optimised via particle swarm optimisation. This approach is designed to be language-independent and is tested here for different languages.

The phrasing model’s performance is assessed per se, by calculating the segmentation accuracy against a golden segmentation. Operational testing also involves integrating the model to a phrase-based Machine Translation (MT) system and measuring the translation quality when the phrasing model is used to segment input text into phrases. Experiments show that the performance of this approach is comparable to other leading segmentation methods and that it exceeds that of baseline systems.

Open access

Yang Cao, R. Samidurai and R. Sriraman

Abstract

This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.

Open access

Amnah Nasim, Laura Burattini, Muhammad Faisal Fateh and Aneela Zameer

Abstract

Cases where the derivative of a boundary value problem does not exist or is constantly changing, traditional derivative can easily get stuck in the local optima or does not factually represent a constantly changing solution. Hence the need for evolutionary algorithms becomes evident. However, evolutionary algorithms are compute-intensive since they scan the entire solution space for an optimal solution. Larger populations and smaller step sizes allow for improved quality solution but results in an increase in the complexity of the optimization process. In this research a population-distributed implementation for differential evolution algorithm is presented for solving systems of 2nd-order, 2-point boundary value problems (BVPs). In this technique, the system is formulated as an optimization problem by the direct minimization of the overall individual residual error subject to the given constraint boundary conditions and is then solved using differential evolution in the sense that each of the derivatives is replaced by an appropriate difference quotient approximation. Four benchmark BVPs are solved using the proposed parallel framework for differential evolution to observe the speedup in the execution time. Meanwhile, the statistical analysis is provided to discover the effect of parametric changes such as an increase in population individuals and nodes representing features on the quality and behavior of the solutions found by differential evolution. The numerical results demonstrate that the algorithm is quite accurate and efficient for solving 2nd-order, 2-point BVPs.

Open access

Md Wasiur Rahman, Fatema Tuz Zohra and Marina L. Gavrilova

Abstract

Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.

Open access

Simone A. Ludwig

Abstract

An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

Open access

Boris Reznikov, Alexander Ruderman and Valentina Galanina

Abstract

The paper considers a discrete state-space model for transients in a three-level flying capacitor DC–DC converter. A transition matrix is obtained for a pulse width modulation (PWM) period. The matrix elements are expanded into a power series using a selected small parameter. The matrix eigenvalues that determine the natural balancing dynamics transients are presented in the form of power series as well. Four separate transients are constructed based on four possible PWM period initial states (topologies). Inductor current and capacitor voltage transients are found for the voltage source power-up as the arithmetic average of the four separate transients. The discrete solutions are replaced by continuous ones. The resulting transients that are elementary functions of the circuit parameters, PWM period, and voltage reference demonstrate good agreement with the simulation results.

Open access

Tibor Vajsz, László Számel and Árpád Handler

Abstract

Motion control is facing an increasing popularity in the present research activities. Owing to the expected wide spreading of motion control applications, it can be predicted that the advancements in the field of electric motor drives will have a high level of influence on the new results in the field of motion control. The synchronous reluctance motor drives mean an excellent and yet cost-effective solution for actuators in motion control applications. In this article, the direct torque control with space vector modulation is analysed as a possible candidate for synchronous reluctance motor motion control applications. Its torque-control performance is investigated as a function of torque-control sample time, and a comparison of the torque ripples is made with other torque-control algorithms by an FFT analysis.

Open access

Tibor Vajsz, László Számel and Árpád Handler

Abstract

Synchronous reluctance motor drives are one of the most attractive alternatives of permanent magnet synchronous motor drives and induction motor drives in the field of conventional industrial and household applications. This tendency is expected to be continued in the case of motion control applications as well. This article investigates two torque-control algorithms that are possible candidates for motion control synchronous reluctance motor applications. The examined torque-control algorithms are direct torque control (DTC) and hysteresis current vector control (HCVC).

Open access

Mallavolu Malleswara Rao and Geetha Ramadas

Abstract

This paper proposes a multiobjective improved particle swarm optimisation (IPSO) for placing and sizing the series modular multilevel converter-based unified power flow controller (MMC-UPFC) FACTS devices to manage the transmission congestion and voltage profile in deregulated electricity markets. The proposed multiobjective IPSO algorithm is perfect for accomplishing the close ideal distributed generation (DG) sizes while conveying smooth assembly qualities contrasted with another existing algorithm. It tends to be reasoned that voltage profile and genuine power misfortunes have generous upgrades along ideal speculation on DGs in both the test frameworks. The proposed system eliminates the congestion and the power system can be easily used to solve complex and non-linear optimisation problems in a real-time manner.

Open access

Ana Camelia Sauca, Tudor Milchiș and Ferdinánd-Zsongor Gobesz

Abstract

A fully 3D numerical analysis of turbulent flow over a cluster of solar photovoltaic (PV) panels was performed in order to assess the total drag and lift forces, comparing the results with the values from the guidelines of the national standard. A Reynolds-Averaged Navier–Stokes (RANS) model was used in the numerical simulations, considering two acting directions of the wind along the length of the array (0 degree – front, and 180 degrees – reverse direction).