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This paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems. [ABSTRACT FROM AUTHOR]

This paper proposes a new block encryption algorithm for cryptographic information protection. It describes a new transformation method EM (Exponentiation Module), which is part of the algorithm, and a method of S-box obtaining. The paper also considers an optimization technique to advance the efficiency of key selection and calculation. We discuss the possibility to obtain good results by applying the peculiar properties of cryptographic primitives in the Galois field. To increase the strength and speed of the encryption algorithm, we used a nonpositional polynomial notation and an indexed view for the Galois field. The paper provides for statistical properties of the ciphertext obtained with the developed algorithm. We also present the results of differential and linear cryptanalysis of the S-box used. [ABSTRACT FROM AUTHOR]

The paper presents the results of a study done to find the optimal shapes of matrix element partitioning on three abstract heterogeneous processors when performing multiplication operations. An abstract processor model allows applying the research results in systems with different heterogeneous architectures. To determine the optimal partitioning shape, the work uses non-rectangular candidate shapes identified by Ashley DeFlumere in her work as a result of applying the technology of redistribution of matrix elements between the processors «push»: Square Corner, Rectangle Corner, Square Rectangle, Block Rectangle, L-Rectangle, Traditional 1D Rectangular. The optimality of shapes is determined for four classes of matrix multiplication algorithms: Serial Communication with Barrier (SCB), Parallel Communication with Barrier (PCB), Serial Communication with Bulk Overlap (SCO) and Parallel Communication with Overlap (PCO). The Hockney model was used to evaluate the communication complexity of algorithms. Mathematical models of the algorithm execution time were introduced in the paper for each considered candidate shape in all algorithms. Based on the developed mathematical models, software was developed that allows to select the form of elements partitioning between processors, depending on the ratio of their speeds and latency of the transmission medium. [ABSTRACT FROM AUTHOR]

Test-suite minimization problem is an essential problem in software engineering as its application helps to improve the software quality. This paper proposes a quantum algorithm to solve the test-suite minimization problem with high probability in O 2 n , where n is the number of test cases. It generates an incomplete superposition to find the best solution. It also handles the non-uniform amplitudes' distribution case for the system with multisolutions. The proposed algorithm uses amplitude amplification techniques to search for the minimum number of test cases required to test all the requirements. The proposed algorithm employs two quantum search algorithms, Younes et al. algorithm for quantum searching via entanglement and partial diffusion to prepare incomplete superpositions that represent different search spaces such that the number of test cases is incremented in each search space, and updated Arima's algorithm to handle the multisolutions case. The updated Arima's algorithm searches for a quantum state that satisfies an oracle that represent the instance of the test-suite minimization problem. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and multimode filters are used for unknown parameters' estimation, and particle filter is used to estimate the posterior density function. Parameter estimation and mode determination could be used in the resampling phase to weight the particles in accordance with the target mode. The main advantage of the adaptive parameter estimation approach is its ability to provide a quick estimation of the abruptly changing parameters from noninformative prior knowledge and to do this for multiple unknown parameters. Simulation results show that the proposed algorithm performs better than the other input estimation and tracking methods. [ABSTRACT FROM AUTHOR]

Cryptography via public key cryptosystems (PKC) has been widely used for providing services such as confality, authentication, integrity and non-repudiation. Other than security, computational efficiency is another major issue of concern. And for PKC, it is largely controlled by either modular exponentiation or scalar multiplication operations such that found in RSA and elliptic curve cryptosystem (ECC), respectively. One approach to address this operational problem is via concept of addition chain (AC), in which the exhaustive single operation involving large integer is reduced into a sequence of operations consisting of simple multiplications or additions. Existing techniques manipulate the representation of integer into binary and m-ary prior performing the series of operations. This paper proposes an iterative variant of sliding window method (SWM) form of m-ary family, for shorter sequence of multiplications corresponding to the modular exponentiation. Thus, it is called an iterative SWM. Moreover, specific for ECC that imposes no extra resource for point negation, the paper proposes an iterative recoded SWM, operating on integers recoded using a modified non-adjacent form (NAF) for speeding up the scalar multiplication. The relative behaviour is also examined, of number of additions in scalar multiplications, with the integers hamming weight. The proposed iterative SWM methods reduce the number of operations by up to 6% than the standard SWM heuristic. They result to even shorter chains of operations than ones returned by many metaheuristic algorithms for the AC. [ABSTRACT FROM AUTHOR]

In this paper, nonlinear model predictive control (NMPC) has been used to control an induction motor (IM). The IM model that is used in the control is third or fifth order model that is based in the vector analysis of the IM. In the third order model, the rotor speed and fluxes are considered as system states where the source frequency and the stator currents are control variables. In the fifth order, the state variables involve stator currents, rotor speed and fluxes where the control variables are stator voltages and source frequency. The formulated nonlinear optimal control problems that are used in the NMPC frameworks are not based on the field ordination technique, however, the input frequency is computed by the MPC optimizer. Moreover, the convergence or stability of the motor speed is guaranteed using stability auxiliary constraint. Simulation studies show the performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Construction projects are delivered in a multidisciplinary environment, which need continues coordination. The aim of this paper is to develop an ANN model to evaluate the influence of coordination factors on construction projects performance. For this purpose, the most effective 16 coordination factors impacting the construction projects performance have been identified. After that, through a questionnaire survey, the extent of coordination factors application and the corresponding project's performance were collected. Three multilayer feed-forward networks with Back-Propagation and Elman-Propagation algorithms were adopted to train, validate, and test the cost, time and quality, as performance evaluation indicators. Consequently, the training process continues unit it reaches the pre-defined error or up to 1000 epochs. The results of Mean Square Error (MSE) confirmed the accuracy of the networks with an average value of 0.0231. Furthermore, the determination coefficient (R2) for the three networks of cost, time, and quality were obtained to be 0.77, 0.76 and 0.75, respectively. [ABSTRACT FROM AUTHOR]

Cardiac or heart sound ca.rries important diagnosis information for several cardiovascular diseases, such as natural or prosthetic valve dysfunction and heart failure. Hence, algorithms are required for the analysis of cardiac sound for computer-based automatic diagnosis. In the cardiac sound-based analysis, one of the tasks is to classify abnormal cardiac sounds, i.e. murmurs, caused by various cardiac anomalies. We introduce a new feature named system response which is chosen to be estimated using wavelet bispectrum over Fourier bispectrum because of non-stationary and non-Gaussian nature of cardiac sounds. System response essentially characterizes the cardiac structure responsible for the production of cardiac sounds which is later employed to automatically classify different types of cardiac murmurs. In cases of various types of cardiac murmurs, such as aortic regurgitation and mitral stenosis, system response of the cardiac structures is studied in this paper. Later, an artificial neural network-based classifier is constructed using the system response as a set of features to automatically classify murmur types. Performance of the classifier is obtained with the sensitivity of 93.57% and specificity of 94.24% which is comparable with the sate of the art. Furthermore, system response computed from wavelet bispectrum shows 4% higher accurate classification than Fourier bispectrum. [ABSTRACT FROM AUTHOR]

The heart pumps the blood around the body to supply energy and oxygen for all the tissues of the body. In order to pump the blood, heart pushes the blood against the walls of arteries, which creates some pressure inside the arteries, called as blood pressure (BP). If this pressure is more than the desired level, we treat it as high blood pressure (HBP). Present days, HBP victims are growing in number across the globe. BP may be elevated because of change in biological or psychological state of a person. In this paper, we considered attributes such as age, anger, and anxiety (AAA) and obesity (+), cholesterol level (+) of a person to predict whether a person is prone to HBP or not. Obesity and cholesterol levels are considered as post-increment of AAA, where obesity as one +, and total blood cholesterol as another + because experimental results reveal that their impact is less comparatively AAA. In our technique, we used different classifiers for prediction, where each classifier considers the impact of each A in AAA along with obesity and cholesterol level of a person to predict whether a person becomes a victim of HBP or not. Random forest algorithm has shown 87.5% accuracy in prediction. [ABSTRACT FROM AUTHOR]

For the purpose of preoperative planning, this paper proposed a modeling algorithm for automatically creating the dynamic model of the generalized body cavity. The algorithm includes two phases. One is the static phase in which the initial cavity model is created from the cross sections; the other is the dynamic phase for modeling the dynamic cavity. In the static phase, the parallel cross sections are generated through slicing the organs enclosing the cavity; then construct the LBCs, and generate the uniform grids; and then create the hexahedron voxels according to the uniform grids; at last, post-processing is used to get the final cavity model represented by triangular meshes. In the dynamic phase, the cavity models at different moments are gained by applying the anchor connection method. We took the human articular cavity as an example to verify the proposed algorithm, and the experimental results showed that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

This paper was aimed at using an adaptive control to develop a backstepping method for a special class of nonlinear systems, which requires no information on the upper bound of parametric uncertainty or disturbance. In this research, a novel control approach, including a developed backstepping method and an adaptive controlling method, is introduced as an adaptive generalized backstepping method (AGBM). Compared to the standard backstepping method, AGBM is far more efficient because the standard method is applicable in strictly feedback systems, but AGBM expands this class of systems. Another advantage of AGBM is that it can be applied to a special class of nonlinear systems with unmatched uncertainty as well as unknown upper bound disturbance. In this method, an adaptive controlling method is used to compensate uncertainty and parametric uncertainty besides stabilizing the controller against disturbances applied to the system. Finally, numerical simulation results demonstrate the advantages and feasibility of the presented algorithm, showing that the AGBM controller guarantees robustness and system stability in the presence of disturbance and uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

In conventional aviation, flight route planning aims to guarantee security and reduce the operating costs of the different types of aircraft that are currently available. This paper presents and analyses the results obtained from planning a flight route in real time for short-range aircraft fitted with geared turbofans with a constant volume combustor to reduce operating costs by optimizing the route in terms of less distance traveled. In the analysis, an off-line route obtained from a flight profile using the Preliminary Multidisciplinary Design Framework tool is compared with the same route that is run in real time using a route planner optimized with a particle swarm optimization algorithm. The average results indicate that the route proposed by the optimization algorithm reduces direct and indirect operating costs if compared to the alternative route (traditional approach according to flight plan). This is mainly due to the lower fuel consumption and maintenance costs when selecting an optimized route. In cases where direct and alternate routes are completely obstructed, the planner can find an optimized route based on the lower costs. In this way, the pilot and the flight controller on the ground can make the decision whether to risk the aircraft with passengers on a route with adverse weather conditions or to have an optimized and safe one. The use of this type of route optimizer leads to significant benefits with respect to aircraft environmental taxes for emissions and noise, and increases aviation safety. This finding indicates that the route planner is a suitable support option when the course of an aircraft must be changed to follow a different route than the suggested alternative. [ABSTRACT FROM AUTHOR]

Studies on the modeling and simulation of the railway vehicle traction system play an active role in the operation and planning phase of railway electrification. In this paper, the longitudinal dynamic of a light rail vehicle was modelled and simulated in Matlab-Simulink. The traction system consists of two parallel motor groups, each of which is composed of two seperately-excited motors connected in series. The first simulation scenario represents how the traction system works in acceleration and braking modes with respect to a given speed change profile. Within this scenario, the time dependent responses of the motor armature and excitation currents, fluxes, motor traction moment, adhesion, resistance forces and acceleration are evaluated, and the constant torque, field attenuation, operation zones and vehicle traction force curve are described. The second simulation scenario represents the slip control application, which examines the complex nonlinear relationship between the adhesion force and the slip ratio, were demonstrated. Modified super-twisting sliding mode slip control are performed under dry, wet and low wheel-rail contact conditions, which are sequentially switched. It has been confirmed by the simulation results that the proposed control strategy achieves the maximum adhesion force of the train. The main purposes of this study are to investigate the operation principles of the railway dynamics associated with acceleration or braking modes and to examine the effects of certain parameters related with the dynamical electromechanical traction system. [ABSTRACT FROM AUTHOR]

The paper proposes a method for S-matrix generalization to minimize discontinuities between one-axis rectangular guides in terms of reflection and transmission coefficients. We modelate successfully rectangular metal waveguide filters with geometrical and physical discontinuities, and a succession of heterogeneous media such periodic structure and containing Meta materials are presented. Method of S-matrix generalization based on modal development operating by breaking down electromagnetic fields on orthonormal basis for each formed discontinuity. Proposed hybrid method that reference to finite element method incorporation is discussed. We conclude that the Galerkin method application leads directly to the matrix of junction diffraction. [ABSTRACT FROM AUTHOR]

Optimal short-term hydrothermal generation cooperation (OSTHTGC) is used to determine the optimal operation strategy for cascaded hydropower plants and thermal plants. In this problem, equality constraints consisting of water time delay, reservoir volume, continuity water and power balance, as well as inequality constraints related to the limits of thermal and hydro generations are taken into account. In this paper, a Cuckoo Bird-Inspired Meta-Heuristic Algorithm (CBIA), which is advanced by using only few control parameters and high success rate, has been proposed to solve the OSTHTGC problem. The proposed method is applied for four test cases where the valve point loading effects of thermal units and the combination operation of a four-cascaded reservoir system are considered. The comparison of obtained results has revealed that that CBIA method is an efficient method with high quality solution and fast convergence speed compared to other relevant approaches. [ABSTRACT FROM PUBLISHER]

Early augmented reality (AR) community has paid a lot of attention to identifying 2D fiducial markers in AR scene, only a little works have been devoted to 3D objects perception. With the massively increased usage of 3D representation, it is highly desirable that powerful processing tools and algorithms should be developed for 3D recognition purpose in the augmented reality domain. In this paper, we address the 3D depth understanding problem for AR assembly applications. We propose a 3D registration based perception method designed for desk assembly environments. The 3D perception method involves two tasks: the optical flow based structure from motion (SFM) and a coplanar 4 points set registration of the SFM point cloud and CAD models point cloud. From the transformation, the position and orientation of the real industrial object in the assembly environment can be determined and an AR operation can be applied. The novelty of our method resides in that it does not require any marker attached on the scene and obtain a more accurate registration result. [ABSTRACT FROM PUBLISHER]

This paper makes a comparative assessment of three popular maximum power point tracking (MPPT) algorithms used in photovoltaic power generation. A 120 Wp PV module is taken as reference for the study that is connected to a suitable resistive load by a boost converter. Two profiles of variation of solar insolation at fixed temperature and varying temperature at fixed solar insolation are taken to test the tracking efficiency of three MPPT algorithms based on the perturb and observe (P&O), Fuzzy logic, and Neural Network techniques. MATLAB/SIMULINK simulation software is used for assessment, and the results indicate that the fuzzy logic-based tracker presents better tracking effectiveness to variations in both solar insolation and temperature profiles when compared to P&O technique and Neural Network-based technique. [ABSTRACT FROM AUTHOR]