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The increase in the number of vehicles on roads has compounded the difficulty in parking them when people go out for movies, shopping, theatre, etc. Thus, this paper proposes smart parking system. The reservation of the parking slots can be made using this proposed smart parking system. Unlike the methods proposed in the existing research, the system proposed in this paper divides the parking area into 3 parts. One for conventional parking, one for vehicles with reservation and the other for the vehicles with and without reservation. Learning automata is used in the proposed system to determine the percentage of the parking area for conventional parking. In general, the proportionate of the slots for reserved parking, conventional parking and common slots need to be assumed. Learning Automata helps in determining this proportionate optimally. The AES-256 encryption algorithm is used to provide security for the details provided by the user during reservation process. Goodput value is maintained for each vehicle which increases or decreases the chances of getting a reservation. The time limit for the reservation of the parking slot is also maintained after which the reservation gets cancelled automatically. Markov Model is used to represent the system. The performance of the proposed algorithm, Learning Automata and Reservation based Secure Smart Parking System (LA-RSSPS) is simulated and evaluated in terms of average waiting time, search time, the probability with which the vehicles do not get parking slot when they do not have reservation and the probability with which the vehicles do not get reservation and is compared with ProNet and iERS. Results have shown that our scheme preforms better than the ProNet and iERS competing schemes. The comparison of analytical and simulations results are also presented. [ABSTRACT FROM AUTHOR]

With the development of 3D technology, the construction of 3D motion model has become an important research issue in many fields, such as virtual reality, animation and robot vision. In this paper, a 3D motion model construction method based on simulation algorithm is proposed. Firstly, the gravity simulation algorithm is used to simulate the trajectory of the object, and the position data of multiple moments with numerical accuracy of 10cm are obtained. Then, the interpolation algorithm is used to interpolate these position data into continuous curves, and the object trajectory with 1cm accuracy is obtained. Finally, we integrate the obtained trajectory data with 3D modeling software to generate a 3D motion model with real motion effect. The experimental results show that this method has high accuracy and reliability in constructing 3D motion model, and is suitable for many fields. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a speed sensorless control scheme named as finite control set-model predictive current control (FCS-MPCC) using a modified fictitious ohmic quantity (R) based model reference adaptive system (MRAS) for grid-connected doubly-fed induction machine (DFIM) drive. The variables of the reference model of this speed sensorless scheme (R-MRAS) are represented in stationary reference frame while those for the adaptive model are denoted in synchronously rotating reference frame. The sensorless formulation thus obtained is completely independent of any stator/rotor resistance terms. The scheme is also devoid of any stator/rotor flux estimation. Moreover, the intuitiveness of FCS-MPCC brings in additional flexibility in comparison to the conventional control techniques like field oriented control (FOC) and direct torque control (DTC). The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. The proposed control algorithm is simulated and tested for limited speed range application in MATLAB/Simulink. The validation of simulation results are further done by experimentation on a dSPACE-1103 based DFIM laboratory setup. Highlights • In this paper, MRAS based sensorless speed estimation of DFIM drive by FCS-MPCC is reported. • The estimation scheme insensitive to the changes in machine parameters. • The overall scheme demonstrates faster execution time than FOC/DTC based control of DFIM drive. [ABSTRACT FROM AUTHOR]

The indirect identification of the geometric errors (GEs) in the rotary axis of a machine tool yields six equivalent GEs (EGEs) that are position-dependent; through an analytical proof, this study demonstrates that these errors also represent four position-independent GEs of the axis. Moreover, a novel algorithm using ball bar measurements to calculate the EGEs of a nutating rotary B -axis and a rotary C -axis is presented herein. This paper also presents a new analytical solution for the actual inverse kinematics of a nonorthogonal five-axis machine tool; this solution is used for GE compensation. The presented algorithms are implemented in a self-developed software that alters the nominal numerical control code in order to eliminate GEs. The compensation accuracy and efficiency are tested using a simulation system. The results demonstrate that the proposed compensation algorithm eliminates all identified GEs. Lastly, a cutting test executed on a machine confirms that the proposed algorithms considerably improve machining accuracy. [Display omitted] • Nonorthogonal five-axis machine tool (NOFAMT) with a nutating rotary B -axis. • Equivalent errors (EGEs) of rotary axes. • Proof that six EGEs of a rotary axis represent ten GEs of this axis. • Identification of the EGEs of the rotary axes measured indirectly. • Compensation for GEs of the NOFAMT analytically. [ABSTRACT FROM AUTHOR]

• A new approach based on reinforcement learning is proposed to improve the environmental adaptability of coherent combining systems. • Based on the deep Q-Network reinforcement learning architecture, the physical information is embedded in the parameters of the reinforcement learning algorithm by combining the physical model of the CBC system, and the parameters are exhaustively analyzed and redesigned. • By redesigning all the parameters, the method can be trained to converge in a very short time with excellent phase correction ability. In contemporary times, the utilization of high-power lasers is progressively expanding. However, the potential of a single laser to generate power confronts inherent physical constraints, leading to limitations in its incremental power augmentation. To address this challenge, coherent beam combining technology has been introduced as a means to achieve amplified power output. In this paper, an intelligent phase control method for coherent beam combining is proposed, which introduces reinforcement learning algorithm to provide assistance for the control problem, designs algorithmic variables based on the physical model of coherent beam combining, deploys it in the environment for multiple interactions, and updates the neural network in real time using the feedback from the environment. The efficacy of the proposed algorithm is validated in a 7-channel coherent beam combining simulation system, and the experiment results show that the synthesized beam is strong and stable under the control of the algorithm, and the convergence of the algorithm as well as the control effect is discussed and validated in different environments, and its performance also exceeds that of the existing mainstream phase control algorithm SPGD. [ABSTRACT FROM AUTHOR]

Measurement, data transfer, modelling, controller systems are the main subjects of interdisciplinary area during prototyping of marine automatic control systems. Experimental parameter identification is an essential step for modelling and control system design are in question for various marine applications. The selection of variables to be measured, type of measurement sensors, type of control algorithms and controller systems, communication, signal conditioning are all important topics for parameter identification and real-time control applications in maritime engineering. The objective of this paper is to present a brief review of these important topics based on our case studies, such as ship roll motion reduction control, optimal trim control of a high speed craft, and dynamic position control of underwater vehicles. These projects involved extensive dynamic modelling, simulation, control algorithm design, real-time implementation and full-scale sea trials. In this paper, the presented methods, and the required characteristics of the marine control systems are demonstrated with the results obtained by the simulation studies and full-scale sea trials. Also, insight into the selection of hardware and software components for mechatronic applications in marine engineering is provided. [ABSTRACT FROM AUTHOR]

This paper designs and optimizes a location hole for contactless power transmission (CPT) system on autonomous underwater vehicles (AUV) hull. Through mechanic coordination, AUV could keep stable and high efficiency for charging and data transmission. Optimization target is to minimize drag coefficient and satisfy volume constraint. A simulation-based computing framework is established for drag coefficient calculation, including geometric parameterization, mesh auto-generation and solver. In the framework, 3D structured mesh is adapted for accurate and efficient calculation, which is a major difference from previous researches. Optimization algorithm is based on surrogate model with parallel sampling strategy. Three sampling methods pick targeted samples and gain responses through parallel computing at each iteration. Results show that initial design doesn't increase much resistance, nearly 3%. Drag coefficient of optimized hull is 2.26% larger than that without hole, a little smaller than initial design. However, on the basis of initial increment of drag, optimized design decreases nearly 20%. This paper also discusses effect of depth and length of location hole on drag and results show greater depth and smaller length contribute to drag reduction. The framework and optimization algorithm can also be employed to optimize location and shape of other appendages on AUV. [ABSTRACT FROM AUTHOR]

Despite the concerns of time trend in subject profiles, the use of Bayesian response adaptive randomization (BRAR) in large multicenter phase 3 confirmative trials has been reported in recent years, motivated by the potential benefits in subject ethics and/or trial efficiency. However three issues remain unclear to investigators: 1) among several BRAR algorithms, how to choose one for the specific trial setting; 2) when to start and how frequently to update the allocation ratio; and 3) how to choose the interim analyses stopping boundaries to preserve the type 1 error. In this paper, three commonly used BRAR algorithms are evaluated based on type 1 error, power, sample size, the proportion of subjects assigned to the better performing arm, and the total number of failures, under two specific trial settings and different allocation ratio update timing and frequencies. Simulation studies show that for two-arm superiority trials, none of the three BRAR algorithms has predominant benefits in both patient ethics and trial efficiency when compared to fixed equal allocation design. For a specific trial aiming to identify the best or the worst among three treatments, a properly selected BRAR algorithm and its implementation parameters are able to gain ethical and efficiency benefits simultaneously. Although the simulation results come from a specific trial setting, the methods described in this paper are generally applicable to other trials. [ABSTRACT FROM AUTHOR]

This paper presents a new extremum-seeking controller (ESC) derived from a statistical interpretation of conventional ESC. The proposed ESC replaces the gradient feedback variable with a normalized correlation coefficient. The algorithm, as presented, can be fully configured with knowledge of only the open-loop response time of the system being optimized and the amplitude of the dither signal. Simulation tests show that the new algorithm has fast convergence times compared with other types of ESC algorithms. The paper also shows that the proposed method is not limited to a periodic dither signal and that it can also utilize a stochastic signal to similar efficacy. The purpose of the paper is to describe the new algorithm and present a practical implementation along with results from simulation tests. [ABSTRACT FROM AUTHOR]

Abstract: Interactions among competing units are crucial to maintaining biodiversity, and non-hierarchical interactions can promote biodiversity in cyclic competing systems. In the present study, we explore the role of hierarchical interactions, existing ubiquitously in reality, in the co-evolution of a cyclic competing system. In systems composed of cyclic competing species with hierarchy interactions in which one predator species has more than one prey, we find that hierarchy disappears in a rather short evolving time. In the process of co-evolution, a hierarchical competing system tends to transit to a cyclic non-hierarchical competing system described by the rock–paper–scissors game. In other words, the cyclic competing interactions appear to eradicate hierarchy. This conclusion is analyzed by a mean-field approach and is tested by stochastic simulations. [Copyright &y& Elsevier]

The reliability analysis of engineering structural systems with limit state functions defined implicitly by time-consuming numerical models (e.g. finite element analysis structural models) requires the use of efficient solution strategies in order to keep the required computational costs at acceptable levels. In this paper, an adaptive Kriging surrogate model with active refinement is proposed to solve component reliability assessment problems (i.e. involving one single design point) with nonlinear and time-consuming implicit limit state functions with a moderate number of input basic random variables. The proposed model, in the first stage, uses an adaptive Kriging-based trust region method to search for the design point in the standard Gaussian space and predict an initial failure probability based on the first-order reliability method as well as sensitivity factors for the input basic random variables. This initial prediction is then verified or improved efficiently in a second stage using Monte Carlo simulation with importance sampling based on a Kriging surrogate model defined iteratively around the design point using an active refinement algorithm. A convergence criterion that detects the stabilization of the failure probability prediction during the active refinement process is also proposed and implemented. The usefulness of the proposed adaptive Kriging surrogate model in terms of accuracy and efficiency for reliability assessment of engineering structural systems is shown in the paper with two relevant numerical examples, involving a highly nonlinear analytical limit state function in two-dimensions and an advanced nonlinear finite element analysis structural model in a larger dimensional space. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf. This proposed operator is applied to the simulation of the hunting process in the algorithm and has 5 variants that are explained in more detail in this paper (centroid, weighted, based on the fitness and two variants using fuzzy logic). Notably the variants having the greatest impact in the GWO performance are based on the use of fuzzy logic. We also present the motivation and results of experiments, as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a comparison among all methods for 30, 64 and 128 dimensions and we conclude that the performance of the Hierarchical GWO algorithm is better when using a fuzzy variant of the hierarchical operator. [ABSTRACT FROM AUTHOR]

Psychological factors affect the movement of people in the competitive or panic mode of evacuation, in which the density of pedestrians is relatively large and the distance among them is small. In this paper, a crowd is divided into groups according to their social relations to simulate the actual movement of crowd evacuation more realistically and increase the attractiveness of the group based on social force model. The force of group attraction is the synthesis of two forces; one is the attraction of the individuals generated by their social relations to gather, and the other is that of the group leader to the individuals within the group to ensure that the individuals follow the leader. The synthetic force determines the trajectory of individuals. The evacuation process is demonstrated using the improved social force model. In the improved social force model, the individuals with close social relations gradually present a closer and coordinated action while following the leader. In this paper, a grouping algorithm is proposed based on grid density and relationship via computer simulation to illustrate the features of the improved social force model. The definition of the parameters involved in the algorithm is given, and the effect of relational value on the grouping is tested. Reasonable numbers of grids and weights are selected. The effectiveness of the algorithm is shown through simulation experiments. A simulation platform is also established using the proposed grouping algorithm and the improved social force model for crowd evacuation simulation. [ABSTRACT FROM AUTHOR]

Hybrid systems are inherently fragile with respect to perturbations when their state experiences jumps on surfaces. Zero-crossing detection algorithms are capable of robustly detecting the crossing of such surfaces, but, up to now, the effects of adding such algorithms to the system being simulated are unknown. In this paper, we propose a mathematical model for hybrid systems that incorporates zero-crossing detection as well as a hybrid simulator for it. First, we discuss adverse effects that measurement noise and discretization can have on hybrid systems jumping on surfaces and prove that, under mild regularity conditions, zero-crossing detection algorithms can robustify the original system. Then, we show that integration schemes with zero-crossing detection actually compute a robustified version of the fragile nominal model. In this way, we rigorously characterize their effect on solutions to the simulated system. Finally, we show that both the model and simulator are not only robust, but also that the hybrid simulator preserves asymptotic stability properties, semiglobally and practically (on the step size), of the original system. Several examples throughout the paper illustrate these ideas and results. [ABSTRACT FROM AUTHOR]

This paper explores 4D seismic history matching and it specifically focuses on the objective function used during the optimisation with seismic data. The objective function is calculated by using binary maps, where one map is obtained from the observed seismic data and the other is from one realisation of the optimisation algorithm from the simulation model. In order to decide which set of parameters is a relevant update for the simulation model, an efficient way is required to measure how similar these two binary images are, during their evaluation within the objective function. Behind this aspect of quantification of the similarities or dissimilarities lies the metric notion, or the art of measuring distances. Four metrics are proposed with this study, the well-known Hamming distance, two widely used metrics, the Hausdorff distance and Mutual Information and a recent metric, called the Current Measure Metric. These metrics will be tested and compared on different case scenarios, designed in accordance to a real field case (gas exsolution) before being used in the second part of the paper. Despite its simplicity, the Hamming distance gives positive results, but the Current Measure Metric appears to be a more efficient choice to cover a wider range of scenarios, these conclusions remain true when tested on synthetic and real dataset in a history matching exercise. Some practical aspects of binary map processes will be examined through the paper, as it is shown that it is more proper to use a derivative free optimisation algorithm and a proper metric should be more inclined to capture global features than local features. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of speech enhancement and acoustic noise reduction by adaptive filtering algorithms. Recently, we have proposed a new Forward blind source separation algorithm that enhances very noisy speech signals with a subband approach. In this paper, we propose a new variable subband step-sizes algorithm that allows improving the previous algorithm behaviour when the number of subband is selected high. This new proposed algorithm is based on recursive formulas to compute the new variable step-sizes of the cross-coupling filters by using the decorrelation criterion between the estimated sub-signals at each subband output. This new algorithm has shown an important improvement in the steady state and the mean square error values. Along this paper, we present the obtained simulation results by the proposed algorithm that confirm its superiority in comparison with its original version that employs fixed step-sizes of the cross-coupling adaptive filters and with another fullband algorithm. [ABSTRACT FROM AUTHOR]

Particle-based methods based on material models using spheres can provide good approximations for many physical phenomena at both the micro and macroscales. The point of departure for the simulations, in general, is a dense arrangement of spherical particles (sphere pack) inside a given container. For generic domains, the generation of a sphere pack may be complex and time-consuming, especially if the pack must comply with a prescribed sphere size distribution and the stability requirements of the simulation. The primary goal of this paper is to present an efficient algorithm that is capable of producing packs with millions of spheres following a statistical sphere size distribution inside complex arbitrary domains. This algorithm uses a new strategy to ensure that the sphere size distribution is preserved even when large particles are rejected in the growing process. The paper also presents numerical results that enable an evaluation of the proposed algorithm. [ABSTRACT FROM AUTHOR]

All traditional cryptographic algorithms used in the network communication environments, relied on mathematical models and computational assumptions, are actually unsafe and apt by many attackers (quantum and man-in-the-middle attacks). Therefore, nowadays Quantum Key Distribution (QKD) promises a secure key agreement by using quantum mechanical systems. QKD becomes a significant trend of new cryptographic revolution. This paper explains how cryptography exploits the quantum mechanics in order to achieve an encryption/decryption process. Additionally, this paper provides a standard simulation for QKD-BB84 protocol and describes improvement key generation and key distribution mechanisms. Then, validation, results and efficiency of BB84 protocol are presented using different security configurations. Finally, our results indicated that the QKD is susceptible to corporate with different security applications and achieve the key availability for such applications. However, it suffers from the higher rate of authentication cost. [ABSTRACT FROM AUTHOR]

Efficient data delivery accompanied with low end-to-end delay is important in vehicular ad-hoc networks and optimal route selection is vital to improve these performance metrics. Different routing algorithms are proposed in VANETs. This paper reviews and analyzes these algorithms briefly and based on the analysis, a new opportunistic-based routing algorithm (OSTD) is proposed for urban scenarios. The proposed algorithm severely considers the type of vehicular distribution in the calculation of utility function. This utility function is used to evaluate the routes in the network. The reason of such a severe consideration is expressed and evaluated in the paper. Vehicle's driving path predictability is also used in the algorithm to forward the packet to a more suitable next hop, as vehicular mobility is the reflection of human social activity. Simulation results show that OSTD achieves a higher delivery ratio and lower end-to-end delay and packet loss compared to the other well-known protocols. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic form of the portfolio selection problem is established in which the uncertainty of risky assets is considered through a probabilistic optimization problem. To this end, by taking seven portfolios of Shanghai stock as a case study, the mean and standard deviation of daily return values are calculated based on five years of real data. The optimal values corresponding to each random case were then stored as a comprehensive database of system responses. Then, by sorting the resulting optimal values from best to worst, the exceedance probabilities of return and risk rankings were calculated for each portfolio and presented in the form of probabilistic pie charts. The results showed that the portfolio with the highest deterministic rate of return has the highest probability of getting the best return ranking as well. However, since the probability of risk in all cases was calculated, the probability of each portfolio to place in the lower rankings (i.e., ranking 2–7) could also be discussed. Additionally, to check the convergence of the model, the probability values calculated by the Monte Carlo method against the sample size were plotted to ensure the accuracy of the final answer. Eventually, by generating Gaussian random noise and importing it into the model input, probability changes were calculated to assess the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is based on specific applications of Material Flow Theory (MFT) in industrial engineering material flow management (MFM). The accent is put on exploring different possibilities to increase productivity and profit in processing architectures using the MFM approach based on virtual modelling and simulation. In this context the paper presents a case study using virtual modelling of a flexible processing architecture and MFM simulation algorithms for diagnosis and optimization. The virtual model of the processing architecture will be used as a tool for identifying and eliminating the flow concentrators and obtaining an improved productivity of the system. [ABSTRACT FROM AUTHOR]

The "shoeboxing" algorithm is a simplification approach capable of converting a building of any shape into a representative shoebox, with the aim of speeding-up Urban Building Energy Modeling simulations. The procedure works towards accurately predicting both annual energy needs and hourly thermal loads, going beyond the preliminary assessment of buildings' thermal performance at city-scale. Furthermore, the simplification has been particularly developed to work with buildings of complex geometry considering adjacencies and obstructions. After a first validation performed for stand-alone buildings, in this paper, the capabilities of the algorithm are evaluated at district-level on fictional parametrically generated layouts and in different climatic conditions. As a whole, the shoeboxing algorithm properly predicted both heating and cooling needs at building-level in all the considered climatic conditions, yielding annual differences within ±10% and ± 20%, respectively for cooling and heating. Moreover, both annual and hourly deviations showed to be similar in the different climates considered, suggesting that the simplification can be reliably employed worldwide. Finally, the thermal simulation time has been reduced up to 36 times. • District-scale validation of an algorithm for Urban Building Energy Modeling • Buildings are converted in shoeboxes including adjacencies and urban context • The simplification is validated on 64 districts of 324 buildings in three climates • Heating and cooling needs are predicted with differences within ±20% and ± 10% • The algorithm reduced the thermal simulation time up to 36 times [ABSTRACT FROM AUTHOR]

Abstract: Verification of identity based on the analysis of dynamic signatures is an important problem of biometrics. The effectiveness of the verification significantly increases when the dynamic characteristics of signature (e.g. velocity of the pen) are considered. These characteristics are individual for each user and difficult to imitate. The effectiveness of the verification can be further improved by using partitioning. In this paper we propose a new method which uses partitioning. In our method partitions represent areas of high and low speed of signature and high and low pen׳s pressure. All selected partitions are used by our algorithm, but more important in the classification process are these partitions, in which the signatures of the user acquired during training phase are more stable. Moreover, final classification is interpretable. In this paper we present the simulation results of the proposed method for the two databases: SVC2004 and BioSecure Database. [Copyright &y& Elsevier]

Abstract: This paper gives an overview of the Systems Processes-Patterns/Systems Pathology (SP3) project of the INCOSE SSWG (Systems Science Working Group) because we see two new specialties arising from that research program. The project is based on research conducted over the last 300 years on natural systems in the seven natural sciences (astronomy, physics, chemistry, geology, biology, math and computers).1 It seeks to unify that knowledge with systems thinking from human and social systems research. The resulting integrated knowledge base (KB) is overwhelming, even when abstracted to only those 50 principles and pathologies that tell us something experimentally about how natural systems work or don’t work. The SP3 project collects facts in 25 categories (each described in this paper) and hundreds of Linkage Propositions (LPs) that explain how 55 systems processes influence each other to maintain systems health. We suggest a new approach called sysinformatics modeled after the bioinformatics courses that today prepare workers for genetics and systems biology. We need corresponding courses in sysinformatics to prepare workers in systems science, systems engineering, computer-based modeling, and new fields such as sustainability studies. The need for bioinformatics resulted from a range of advances in new experimental tools and techniques that led to terabytes of data that required meaningful analysis to bring from the lab bench to the hospital bed. Similarly, sysinformatics would attempt to make more meaningful the mountains of data and relations resulting from a new science of systems. It will require active participation of the computer sciences, engineering specialties, and deep mathematics. Overall, it would help in understanding of how systems processes work and don’t work. Sysinformatics would be a rigorously transdisciplinary field aimed at the discovery, storage, retrieval, organization, classification, and analysis of systems science data. It would stimulate discovery of new tools and algorithms for analysis of data, encourage invention of new ways to present and display data in ways more easily or intuitively meaningful to humans (like the Circos maps of genomics that interpret many different whole genome changes). Sysinformatics would add data mining, AI, simulation image processing, a range of algorithms, new companies and computerized tools to the toolboxes of practicing systems engineers. This paper describes initial similarities and differences between bioinformatics and sysinformatics. It also introduces “systems mimicry” as a potential new specialty and suggests similarities and differences between biomimicry and systems mimicry. [Copyright &y& Elsevier]

Abstract: Since optical network carries a lot of traffic, survivability is very important to ensure the service continuity against network failures. Recently, multicast services become more popular to meet the huge multimedia demand in network, so that multicast survivability in optical network has got more attention. However, multicast survivability may lead to more energy consumption due to more redundant backup resources allocated. Since greening network is particularly important with the energy crisis keeping growing, green multicast survivability in optical network is a challenging issue. This paper focuses on the green multicast grooming protection problem in optical network and designs two auxiliary graphs that includes a virtual topology graph and an integrated graph to address the survivable multicast grooming, routing and wavelength assignment. Based on the auxiliary graphs, this paper proposes an efficient heuristic called green multicast grooming algorithm based on spanning path dedicated protection (GMG-SPDP), in which the network reliability and the energy efficiency are jointly taken into account. Simulation results show that, compared to other algorithms, the proposed GMG-SPDP not only has lower blocking probability and higher bandwidth utilization ratio, but also saves more energy. [Copyright &y& Elsevier]

Abstract: This paper aims to address the attitude control problem of a rigid spacecraft involving parameter variation and external disturbance. A backstepping based adaptive sliding mode control (B-ASMC) strategy is proposed as a solution, where the upper bounds of the parametric uncertainty and disturbance are not required for the controller design. Compared to current adaptive sliding mode control (ASMC) design, the proposed B-ASMC design has two advantages. First, unlike existing ASMC algorithms, where only the asymptotical stability of the sliding function is achieved, this paper shows that the asymptotical stability of the attitude states, i.e., the attitude parameter and the angular velocity, can be guaranteed by B-ASMC. Second, the system performance is improved due to two additional terms obtained in the backstepping framework. Detailed design principle and rigorous stability analysis are provided. Finally, a large angle Move-to-Rest attitude maneuver is employed in the numerical simulation to verify the effectiveness of the proposed strategy. [Copyright &y& Elsevier]

Abstract: Calibration of accelerometers can be reduced to 3D-ellipsoid fitting problems. Changing extrinsic factors like temperature, pressure or humidity, as well as intrinsic factors like the battery status, demand to calibrate the measurements permanently. Thus, there is a need for fast calibration algorithms, e.g. for online analyses. The primary aim of this paper is to propose a non-iterative calibration algorithm for accelerometers with the focus on minimal execution time and low memory consumption. The secondary aim is to benchmark existing calibration algorithms based on 3D-ellipsoid fitting methods. We compared the algorithms regarding the calibration quality and the execution time as well as the number of quasi-static measurements needed for a stable calibration. As evaluation criterion for the calibration, both the norm of calibrated real-life measurements during inactivity and simulation data was used. The algorithms showed a high calibration quality, but the execution time differed significantly. The calibration method proposed in this paper showed the shortest execution time and a very good performance regarding the number of measurements needed to produce stable results. Furthermore, this algorithm was successfully implemented on a sensor node and calibrates the measured data on-the-fly while continuously storing the measured data to a microSD-card. [Copyright &y& Elsevier]

Abstract: This paper considers a scenario in which signals from an emitter at an unknown location are received at a number of different collinear locations. The receiver can determine the received signal strength, but no other parameters of the signal. Postulating a log-normal transmission model with a constant but unknown path loss exponent and, also, an unknown transmit power and known noise variance, the paper shows how the localization problem can be solved, along with estimating the parameters appearing in the log-normal transmission model, given enough measurements at different points. The log-normal transmission model parameters can be determined first. An algorithm based on construction of a Gram matrix is proposed to estimate the path loss exponent and transmit power parameters from the received noisy power measurements. Since the estimated parameters are biased due to the nonlinearity of the model and constraints, a pattern-matching algorithm is also proposed to remove the bias in the estimates. The distances corresponding to the different received signal strength measurements can then be bounded, and finally the location estimation is formulated as a convex optimization problem where the estimated distances are used as the new measurements. Simulation results are finally provided to assess the efficacy of the proposed methods in the parameter and location estimation. [Copyright &y& Elsevier]

Abstract: The environmental/economic dispatch (EED) is an important daily optimization task in the operation of many power systems. It involves the simultaneous optimization of fuel cost and emission objectives which are conflicting ones. The EED problem can be formulated as a large-scale highly constrained nonlinear multiobjective optimization problem. In recent years, many metaheuristic optimization approaches have been reported in the literature to solve the multiobjective EED. In terms of metaheuristics, recently, scatter search approaches are receiving increasing attention, because of their potential to effectively explore a wide range of complex optimization problems. This paper proposes an improved scatter search (ISS) to deal with multiobjective EED problems based on concepts of Pareto dominance and crowding distance and a new scheme for the combination method. In this paper, we have considered the standard IEEE (Institute of Electrical and Electronics Engineers) 30-bus system with 6-generators and the results obtained by proposed ISS algorithm are compared with the other recently reported results in the literature. Simulation results demonstrate that the proposed ISS algorithm is a capable candidate in solving the multiobjective EED problems. [Copyright &y& Elsevier]

Abstract: This paper presents an evaluation of various control methods to be used during drilling operations where an unexpected gas influx occurs. In the event of an unexpected gas influx the current industry procedure is to control the pressure in the well manually. The drilling industry term for this manual procedure is well control. The focus of the paper is threefold. Firstly, to design an automatic sequence which is similar to the existing manual procedure. Secondly, to evaluate three different control algorithms for pressure control during an unexpected gas influx, and thirdly, to evaluate control parameter tuning needed when implementing different control algorithms. The control methods have been evaluated on various drilling scenarios with unexpected gas influx, referred to as a kick. After a kick of reservoir gas has entered the well, automatic control of the well control choke and rig pump is applied to compensate for pressure fluctuations while circulating out the gas. A PI controller is designed to stabilize the well pressure by controlling the well control choke, an internal model controller (IMC) controls the pressure by manipulating the choke and the rig pump flowrate, and a model predictive controller (MPC) uses coordinated control of the choke and the pump flowrate to stabilize the well pressure. The model based controllers use a simple first order model of the well. Simulations are performed using a detailed flow model of the well to test the controller performance and robustness. Several cases with different amounts of gas influx are investigated. The simulations show that it is feasible to control the pressure using automatic control of the choke valve and pump during an unexpected gas influx by use of all the presented control methods. The control methods are robust against changes in process conditions and disturbances, as they are able to handle several pressure levels and gas volumes without requiring re-tuning. However, since the pressure dynamics in the well are influenced when gas is entering the well, the model based controllers could probably be further improved if the models were updated after the gas influx occurred. The results indicate that adaption of the automatic sequence to the current manual procedure is applicable. However, to avoid a reduction in downhole pressure when stopping the pump and shutting in the well, the automatic sequence may be further improved beyond what is feasible with manual operation. [Copyright &y& Elsevier]

Abstract: Photovoltaic (PV) generation systems (PGSs) have become an attractive option among renewable energy sources because they are clean, maintenance-free and environmental friendly. For PGSs, a simple and fast maximum power point tracking (MPPT) algorithm is essential. Although the static tracking efficiency of conventional MPPT method is usually high, it drops noticeably in case of rapidly changing irradiance conditions. In this paper, two fast and accurate digital MPPT methods for fast changing environments are proposed. By using piecewise line segments or cubic equation to approximate the maximum power point (MPP) locus, two high-speed, low-complexity MPPT techniques can be developed. To make the developed system more convenient for common PGS users, neural network (NN)-based program which can be used to calculate the parameters of the emulated MPP locus is also developed and embedded into the proposed digital MPPT system. Theoretical derivation and detailed design procedure will be provided in this paper. The advantages of the proposed system include low computation requirement, fast tracking speed and high static/dynamic tracking efficiencies. To validate the effectiveness and correctness of the proposed methods, simulation and experimental results of a 230W PV system will also be provided. [Copyright &y& Elsevier]

Abstract: The system of equations arising in finite element simulations of components made of ferroelectroelastic materials is non-linear if the loading is sufficiently high. The Newton–Raphson method represents a widely used iterative technique to solve this system of non-linear equations. However, if the scalar potential formulation is utilised, convergence difficulties may occur. This circumstance can be primarily attributed to the specific form of the non-linear response of typical ferroelectroelastic materials being subjected to electrical loading. The present paper is devoted to modifications of the Newton–Raphson method, which are capable of improving the convergence behaviour experienced in the finite element iteration. We extend an existing modification to the fully coupled, ferroelectroelastic case. Additionally, a new modification of the Newton–Raphson method is proposed. This method applies an iteration algorithm, which is virtually equivalent to the iteration algorithm of the unmodified Newton–Raphson method combined with the vector potential formulation. An important feature of both modifications is that they are applied on the integration point level. Therefore, the global non-linear finite element iteration scheme remains unchanged. Finally, the practicability of the modifications discussed in the paper is shown in a numerical example. [Copyright &y& Elsevier]

Abstract: This paper investigates the design of a linear-in-the-parameters (LITP) regression classifier for two-class problems. Most existing algorithms generally learn a classifier (model) from the available training data based on some stopping criterions, such as the Akaike''s final prediction error (FPE). The drawback here is that the classifier obtained is then not directly obtained based on its generalization capability. The main objective of this paper is to improve the sparsity and generalization capability of a classifier, while reducing the computational expense in producing it. This is achieved by proposing an automatic two-stage locally regularized classifier construction (TSLRCC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial classifier is then generated by the direct evaluation of these candidates models according to the leave-one-out (LOO) misclassification rate in the first stage. The significance of each selected regressor term is also checked and insignificant ones are replaced in the second stage. To reduce the computational complexity, a proper regression context is defined which allows fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. [Copyright &y& Elsevier]

Abstract: To implement an unmanned autonomous obstacle avoidance system, this paper presents a novel distance estimation method based on an improved spherical optical flow algorithm using a panoramic sensor. The method uses a panoramic sensor to extend the field of view. This paper investigates a panoramic sensor imaging model and the optical flow field and then derives the improved spherical optical flow algorithm. Based on the spatial structure characteristics of the panoramic image, the proposed spherical optical flow smoothness constraint is introduced into the spherical optical flow field. Combing the smoothness constraint with the spherical optical flow equation, the spherical optical flow value is solved for. The motion model of a panoramic sensor, which is the distribution of spherical optical flow field with the condition that the panoramic sensor has self-translational motion, is developed. According to the spherical optical flow value, the motion model and the velocity of the panoramic sensor, a distance estimation mechanism and a computational method are proposed for an Unmanned Aerial Vehicle (UAV). Simulations show that the method can extract the distance information effectively and offers good accuracy. [Copyright &y& Elsevier]

Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms. [Copyright &y& Elsevier]

Abstract: The vast majority of Internet services available today rely on the computing capabilities of data centers. The load in a data center varies according to the time of day, week and year, thus it is important to be able to optimize the use of computing resources dynamically and continuously. This paper presents a framework for modeling and simulating the power and performance behavior of data centers, as well as optimization algorithms capable of power and performance trade-offs, considering all cores in a data center simultaneously. The contributions of this paper are threefold. First, it presents a novel concept of Virtual Power States (VPSs) which are combinations of processors power states and utilization rates, which are used for continuous optimization of power and performance. Second, it presents two algorithms (one optimal and one heuristic) for selecting the power states in each core in a data center in order to optimize the overall power and performance. Third, it presents a power and performance simulation model for data centers, and it shows that for a given varying workload, the algorithms can be applied at regular time intervals, change the power states of the CPU cores to adapt to varying workload and power policies, keeping the data center running at near optimal points continuously. Examples using Intel i7-based and AMD Opteron-based web servers are presented. [Copyright &y& Elsevier]

Abstract: This paper presents an effective fault location technique for transmission grids based on phasor measurement units (PMUs). High accuracy in fault location is achieved by using both the fault observability with PMUs placement and an accurate distributed parameter line model for the transmission lines. The work performed in this paper is provided with high performance which is simplicity, sensitivity, stability and featuring robustness to the uncertainties in fault inception angle, load current and fault path resistance, and the accuracy and reliability of the calculation for the technique is very high. This paper illustrates the characteristics of the proposed algorithm and evaluates its performance by using transient fault data obtained from PSCAD/EMTDC simulations. [Copyright &y& Elsevier]

Abstract: In this paper, a new method for robot navigation in dynamic environments, called the reciprocal orientation algorithm, is introduced. This algorithm deals with the case in which each robot moves without direct communication with the other robots. The algorithm suggested in this paper uses the concept of reciprocal orientation which guarantees both smooth and collision-free robot trajectories. The algorithm is implemented in several simulation scenarios, some of them involving tens of robots. The deadlock problem, which occurs between two robots, has been solved by using an adequate deadlock resolution algorithm. [Copyright &y& Elsevier]

Abstract: The prediction of the microstructure evolution is one of the most significant problems in materials science. The objective of this paper is development of principles of the cellular space reorganization and their implementation into the three-dimensional frontal cellular automata (FCA) for modeling of the microstructure evolution during the multi-stage deformation. Motivation for the reorganization and principles governing this process are presented in the paper. The FCA cells do not keep the form of undistorted cubes during the simulation, but they get deformed. That leads to the necessity of space reorganization. Different conditions on the boundaries of the cellular space are taken into consideration as they are closely connected with the developed algorithms of the reorganization. These two variants of the reorganization are described in detail. The first one can be used when cellular space can be reduced; and the second one, when the reduction of modeled volume is not acceptable. A short description of frontal cellular automata is presented in this paper. The model consists of two parts: deformation and microstructure evolution. The independence of the grain growing from the shape and the sizes of the cell is ensured by so-called “virtual front tracking” algorithm. The microstructural part of the model simulates two phenomena: nucleation and growth of new grains. In this paper only static recrystallization is considered. When the recrystallization is simulated, the nucleation and the grain boundary migration depend on the deformation parameters such as: temperature, strain, strain rate, dislocation density and crystallographic orientation. This phenomenon, along with the deformation, can be modeled over a wide range of multi-stage deformation processes. The process of flat rolling is chosen as the simplest example. The data needed for the FCA simulation is received from the simple numerical calculations. The results of the simulation of the microstructure evolution obtained during the last five passes of the flat rolling are also presented in the paper. [Copyright &y& Elsevier]

Abstract: The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown. [Copyright &y& Elsevier]

Abstract: This paper proposes a day-ahead electricity price classification that could be realized using three-layered feed forward neural network (FFNN), cascade-forward neural network (CFNN) trained by the Levenberg–Marquardt (LM) algorithm and generalized regression neural network (GRNN). The electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. These electricity price classifications are important because all market participants do not know the exact value of future prices in their decision-making process. In this paper, various electricity market price classification classes with respect to pre specified electricity price thresholds are used. The simulation results show that the proposed CFNN method provides a robust and accurate method for day-ahead deregulated electricity market price classification classes. The proposed neural network classification models of electricity prices are tested on the electricity markets of mainland Spain and New York. [Copyright &y& Elsevier]

Abstract: Testing algorithms with real cars is a mandatory step in developing new intelligent abilities for future transportation systems. However, this step is sometimes hard to accomplish especially due to several problems. It is also difficult to reproduce the same scenario several times. Besides, some critical and/or real world forbidden scenarios cannot be tested. Thus, the comparison of several algorithms using the same experimental conditions is hard to realize. Considering that, it seems important to use simulation tools to perform scenarios with realistic conditions. The main problem with these tools is their distance from real conditions, since they deeply simplify the reality. This paper presents the architecture of the simulation/prototyping tool named Virtual Intelligent Vehicle Urban Simulator (vivus). The goal of vivus is thus to overcome the general drawbacks of classical solutions by providing the possibility of designing a vehicle virtual prototype with simulated embedded sensors and physical properties. Experiments made on linear platoon algorithms are exposed in this paper in order to illustrate the similarities between simulated results and those obtained with real cars. [Copyright &y& Elsevier]

Abstract: In this paper, direct torque control (DTC) algorithms for double star permanent magnet synchronous machine alimented by two inverters is described. The double star permanent magnet synchronous machine has two sets of three-phase stator windings spatially shifted up by an angle γ=300. The double star permanent magnet synchronous is used in areas of high power industrial applications such as traction and naval propulsion. Because constitute an advantageous choice compared to classical synchronous machine, because of the relatively low torque produced. This machine is controlled by tree level direct torque with speed regulator PI, replaced by the sliding mode regulator to improve the results obtained. The implementation of the DTC multi-level applied to a double star permanent magnet synchronous machine is validated with simulated results. In this paper a method for modeling and simulation of double star permanent magnet synchronous motor drive MATLAB/Simulink. [Copyright &y& Elsevier]

Abstract: This paper aims to present a set of lost-in-space star identification algorithms that works effectively for small satellites. Several algorithms are investigated for both phases of star identification: feature extraction and catalogue search. For feature extraction, it is shown that an algorithm using extended images with combined images works best, and for catalogue search, the Group Catalogue is shown to be most efficient. Therefore, this paper proposes an algorithm combining these three. Simulation results show the effectiveness of this algorithm for its robustness to various errors, and an application of this algorithm, “unequal” star trackers, is introduced. [Copyright &y& Elsevier]

Abstract: High strength thin-walled rectangular concrete-filled steel tubular (CFST) slender beam-columns under eccentric loading may undergo local and overall buckling. The modeling of the interaction between local and overall buckling is highly complicated. There is relatively little numerical study on the interaction buckling of high strength thin-walled rectangular CFST slender beam-columns. This paper presents a new numerical model for simulating the nonlinear inelastic behavior of uniaxially loaded high strength thin-walled rectangular CFST slender beam-columns with local buckling effects. The cross-section strengths of CFST beam-columns are modeled using the fiber element method. The progressive local and post-local buckling of thin steel tube walls under stress gradients is simulated by gradually redistributing normal stresses within the steel tube walls. New efficient Müller''s method algorithms are developed to iterate the neutral axis depth in the cross-sectional analysis and to adjust the curvature at the columns ends in the axial load–moment interaction strength analysis of a slender beam-column to satisfy equilibrium conditions. Analysis procedures for determining the load–deflection and axial load–moment interaction curves for high strength thin-walled rectangular CFST slender beam-columns incorporating progressive local bucking and initial geometric imperfections are presented. The new numerical model developed is shown to be efficient for predicting axial load–deflection and axial load–moment interaction curves for high strength thin-walled rectangular CFST slender beam-columns. The verification of the numerical model and parametric studies is given in a companion paper. [Copyright &y& Elsevier]

Abstract: This paper presents a dynamic model for Solid Oxide Fuel Cell (SOFC) stack using Local Linear Model Tree (LOLIMOT) algorithm which is useful for both dynamic and steady-state studies. Most of conventional SOFC models require a large number of parameters and factors, which are difficult to be determined or estimated. In this paper, a LOLIMOT-based model, which does not require the parameters of fuel cell, is proposed for each operation mode of SOFC. In these models, decision tree-based feature selection approach is exploited to select inputs of the LOLIMOT. The proposed models are trained in a short time and they have little errors. In order to illustrate the effectiveness of the proposed models, they are applied to a simulated model of a 5-kW SOFC stack which show satisfactory results for both steady-state and dynamic studies. Furthermore, the proposed model demonstrates convincing results for real-time simulation studies. [Copyright &y& Elsevier]

Abstract: This paper described the structure of a Shunt Active Power Filter system and compared various methods of active power filter harmonic detection, then the improved adaptive algorithm was used in active power filter harmonic detection; as regards the control strategy, the control method that based on optimal voltage space vector hysteresis current was presented in this paper for tracking control, And by using Matlab/Simulink simulation software, the Adaptive filter detection system and control model of Three-phase three-wire Shunt Active Power Filter were established. Simulation results showed that the selected detection method and control strategy of harmonic current played a fairly good compensation role on the harmonics which was generated by nonlinear loads and had a good dynamic compensation performance. [Copyright &y& Elsevier]

Abstract: For ill-posed inversion problem of dynamic light scattering (DLS), this paper is discusses DLS inversion of GMRES algorithm. Combining Tikhonov regularization inversion strategy with GMRES algorithm, a GMRES-TIK inversion algorithm is designed in this paper. The simulation datum was respectively inversed by two methods. The inversion results demonstrate that, correct inversion results can’t obtained by GMRES algorithm, however inversion results of GMRES-TIK algorithm is reasonable under a certain noise level. Therefore, we can draw conclusions that GMRES algorithm can’t directly used for DLS inversion, after combining Tikhonov regularization method, it can used for DLS inversion and has strong noise immunity. [Copyright &y& Elsevier]