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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]

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]

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]

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]

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]

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]

Highlights • Approximately achieve tradeoff between capacity and Jain's fairness that cannot increase fairness while not decrease capacity. • Analog beamforming and user grouping are designed to suppress interference. • Complexity is comparable to most of the existing scheduling approaches. Abstract This paper investigates user scheduling in millimeter-wave multiple-input multiple-output systems that can achieve a tradeoff between capacity and Jain's fairness index. By decomposing the original problem into digital beamforming design, duration allocation, and analog beamforming design, each subproblem is solvable. It is proved that the duration allocation problem possesses a property such that the convex optimization algorithm can be employed for the near optimal result. Moreover, the analog beamforming vectors and the user grouping are designed to avoid interference caused by the reuse of the same subspace. Finally, the computational complexity of the proposed approach is analyzed and is compared with that of other approaches. The simulations verify that the proposed approach can achieve the highest fairness and a higher capacity than approaches of close fairness. [ABSTRACT FROM AUTHOR]

Highlights • An interferogram simulation algorithm for off-axis conic surfaces is proposed. • The algorithm is used in stressed mirror polishing technique. • The described procedure gives information in real time in a quantitate way. • The procedure helps to control the fabrication process for off-axis optical surfaces. • Irregularity data and aberration coefficients of the surface is obtained. Abstract Off-axis conic sections optimizes the design of optical instruments, allowing unrestricted access to the focal point, and making possible to analyze the deflected light without disturbing the incident beam. In this paper are derived, as a first aim, a set of equations representing all kind of conical surfaces and their off-axis sections. With these analytical expressions, we found the associated wavefront coming from the off-axis sections. To complete this analysis, an algorithm was developed to obtain simulated interferograms for off-axis optical surfaces and was used in combination with interferometry to optical test this kind of surfaces in a quantitative way, an important aspect in optical fabrication, specifically the stressed mirror polishing technique. To test the algorithm and the derived equations, we compared experimental interferograms against interferograms calculated with our algorithm. Finally, we found that our method can calculate the aberration coefficients for any off-axis conical surface in a satisfactory manner, giving us the necessary data to control the fabrication process. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes to confront a mixed domain decomposition method with industrial computations, in particular the simulation of quasi-static assemblies with frictional contact between the parts. The method is implemented in a non-invasive manner around an industrial finite element software. The performance of the algorithm is studied on industrial assemblies. Highlights • Automatic choice of the mixed parameters based on bounding boxes to approximate the size of subdomains. • A description of the non invasive parallel implementation of the method with the software salome and code-aster. • Applications to complex structures given by our industrial partners. It involves pre-loaded bolts with frictional contact. [ABSTRACT FROM AUTHOR]

Highlights • Descriptions of the experimental tests of two large-size RC containment specimens. • Descriptions of CSMM-based shell element for simulation of RC shell-type structures. • Nonlinear FE analysis of the RC containments using CSMM-based shell element. • Discussions and comparisons of experimental outcomes and analytical results. Abstract The nuclear containment structure is one of the most important infrastructure systems ensuring the safety of a nuclear power plant. The structural behavior of a cylindrical containment structure made of reinforced concrete (RC) with large dimensions and numerous rebars is complex and difficult to predict. The complex behavior of the RC containment structure has been investigated in an international collaboration project between the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan and the University of Houston (UH), Houston, Texas. At NCREE two 1/13 scaled cylindrical RC containment specimens were tested under reversed cyclic loads [1]. At UH, a finite element simulation of the two tested specimens was developed using a finite element analysis (FEA) program SCS [2]. In the program, a new shell element, the so-called CSMM-based shell element, was developed based on the Cyclic Softened Membrane Model [3] and the formulation of an 8-node Serendipity curved shell element [4] with a multi-layer approach [5]. The UH simulated seismic behavior was close to the NCREE experimental results. This paper presents the theoretical development of the FEA program SCS and the comparisons of its predictions with the experimental structural behavior of the two RC containment specimens. This simulation model and the FEA program are excellent tools to develop effective performance-based design provisions. [ABSTRACT FROM AUTHOR]

Highlights • Quantitative approach to select master degrees of freedom in shear building models. • Massive simulation were executed and their results analyzed. • Three modal error norms and one displacement error signal norm are defined. • Symmetric choice of master degrees of freedom yields the smallest errors. • Results quantitatively confirm "educated" choices made in Civil Engineering. Abstract A quantitative criteria to select master degrees of freedom when identifying reduced order models (ROMs) of one and three dimensional building models is proposed. The goal is achieved by performing intensive simulations of seismic responses of a large set of regular shear building models, that is constructed around a nominal model, whose inter-story mass and stiffness are randomly modified. Different levels of model reduction are selected and, for each one, an exhaustive selection of master degrees of freedom is realized. The parameters corresponding to all the ROMs are recovered by applying recursive least squares (RLS). Later, the response of the full order model (FOM) is compared with each one of the multiple ROMs response to the same seismic excitation signal, using for that purpose three error norms related with modal information: period, mode shape and participation factors and one error norm related to displacement errors. To analyze results, ROMs are classified in three groups that depend on their selection; the first group corresponds to lower-middle-upper story selection, the second group for lower-upper stories and the third group to all other possible cases. Results indicate first that the modal error norms can be used as a good predictor of the displacement error norms and second that symmetric selections of stories in the ROMs are the most accurate. These results are consistent with qualitative or "educated" choices made normally in Civil Engineering, that however, lacked a quantitative criteria similar to the one presented in this paper. Finally, a simulated example where a twelve stories model is reduced to a three stories model is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

Highlights • We propose an improved diffusion total least-squares (DTLS) algorithm, where the estimated value at each node is passed through a first order recursive filter with adjustable parameter in order to enhance the identification performance. • We develop robust adaptive strategies to update various internal system parameters of the proposed DTLS algorithm. • The convergence behavior of the adaptive weight vector and related system parameters of the new algorithm are analyzed by means of Lyapunov stability theory. • Simulation results in the context of distributed system identification demonstrate the superiority of the proposed DTLS algorithm. Abstract The errors-in-variables (EIV) system model has been widely studied under the assumption that both input and output signals are contaminated with noise. For the in-network distributed linear system identification problem under the EIV model, the total least-squares (TLS) approach which has the ability to minimize perturbations in both input and output signals offers an efficient solution. In this paper, we propose an improved diffusion total least-squares algorithm, where the estimated (i.e. filtered) value at each node is passed through a first order recursive filter with adjustable parameter in order to enhance the identification performance. The resulting outputs from all the nodes are subsequently used to adapt the unknown linear system weight vector in real-time through a cooperative diffusion scheme based on the adapt-then-combine (ATC) policy. We also present robust adaptive strategies to tune various internal system parameters, such as the steps sizes, normalization factors, etc., under practical conditions of operation. The convergence behavior of the adaptive weight vector and related system parameters is analyzed by employing Lyapunov stability theory. Simulation results for various distributed system identification scenarios demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract As one of the most advanced non-destructive analytical techniques for nuclear wastes, tomographic Gamma Scanning (TGS) is able to give accurate quantitative and qualitative measurements of nuclear waste barrels. OSEM (Ordered Subsets Expectation Maximization) has been used in transmission TGS image reconstruction on account of its good reconstruction quality and ideal convergence rate. In this paper, an improved method—NMO-OSEM (Non-minimization optimization OSEM) was proposed, it's an iterative algorithm with corrected initial values optimized by non-minimization optimization method. To evaluate its performance, a TGS system is used to perform transmission measurements on barreled nuclear wastes. The results show: ①Compared with the reconstructed images by traditional OSEM under 6 transmission energies (122 keV, 344 keV, 779 keV, 964 keV, 1112 keV, 1408 keV), the improved NMO-OSEM has a great advantage in reducing the artifacts and effectively improving the quality of the reconstructed images. ②the attenuation coefficients values of 72 voxels under 3 emission energies (662 keV, 1173 keV and 1332 keV) reconstructed by the proposed algorithm are more accurate (range of error: 0.22%–13.83%) than reconstructed by traditional OSEM (range of error: 1.31%–32.21%), which proves this method has more stable reconstruction precision and could be regarded as an ideal option for real applications. Highlights • An improved initial value optimization algorithm (NMO-OSEM) is proposed for transmission reconstruction in TGS. • A TGS system is adopted for transmission measurements on simulated barreled nuclear wastes. • NMO-OSEM can preferable suppress image noise and reduce image artifacts. • Reconstructed values of NMO-OSEM has smaller numerical error (0.22%-13.83%) than that of traditional OSEM. [ABSTRACT FROM AUTHOR]

Highlights • A traffic accident model based on self-exciting point processes is proposed. • The stationary condition, reliability and safety analysis under a traffic model are investigated. • The related probability and characteristics are presented by the simulation methods. • The MLEs for the parameters in a traffic accident model for two data situations are given. Abstract Traffic accidents may pose a high risk with respect to human travel safety. In order to improve the safety of travelling, an insight into the traffic accidents is needed. This requires a practical and specifically approach when it comes to modelling for traffic accidents. This paper presents a traffic accident model based on self-exciting processes, which describes the specific phenomena and problems in traffic accidents. The research focuses on the stationary condition, reliability and safety analysis of the traffic network in a certain city or the traffic situation in a certain area. First, a traffic accident model based on self-exciting processes is developed, and secondly, the stationary condition of the model is studied, which is used to analyze the reliability and safety of the traffic network in a certain city or the traffic situation in a certain area. In addition, the probability and related characteristics for traffic process are evaluated by simulation methods. The traffic accident model is developed with the use of self-exciting processes. Maximum Likelihood Estimate (MLE) is used for the parameter estimations of the traffic accident model. Finally, a numerical example is presented, in which the traffic accident model developed is applied to the simulation data. [ABSTRACT FROM AUTHOR]

Highlights • We propose to combine different advanced numerical methods in one algorithmic recipe for EDS-STEM tomographic reconstruction. • Combining the methods facilitates tailoring the algorithm for specific samples and datasets. • Compared to using these methods separately, tailored combined recipes improve the accuracy of reconstructions from limited data. Abstract Energy-dispersive X-ray spectroscopic (EDS) tomography is a powerful three-dimensional (3D) imaging technique for characterizing the chemical composition and structure of nanomaterials. However, the accuracy and resolution are typically hampered by the limited number of tilt images that can be measured and the low signal-to-noise ratios (SNRs) of the energy-resolved tilt images. Various sophisticated reconstruction algorithms have been proposed for specific types of samples and imaging conditions, yet deciding on which algorithm to use for each new case remains a complex problem. In this paper, we propose to tailor the reconstruction algorithm for EDS tomography in three aspects: (1) model the reconstruction problem based on an accurate assumption of the data statistics; (2) regularize the reconstruction to incorporate prior knowledge; (3) apply bimodal tomography to augment the EDS data with a high-SNR modality. Methods for the three aspects can be combined in one reconstruction procedure as three modules. Therefore, a reconstruction algorithm can be constructed as a 'recipe'. We also provide guidelines for preparing the recipe based on conditions and assumptions for the data. We investigate the effects of different recipes on both simulated data and real experimental data. The results show that the preferred recipe depends on both acquisition conditions and sample properties, and that the image quality can be enhanced using a properly tailored recipe. [ABSTRACT FROM AUTHOR]

Abstract As one of the most common user interactive behaviors in many social media services, mention plays a significant role in both user interaction and information cascading. While an increasing line of work has focused on analyzing the mention mechanism for information diffusion, the essential problem of mentionee recommendation from the perspective of common users, i.e., how to find mentionees (mentioned users) who are most likely to be notified by a mentioner (mentioning user) for knowing a post, has been seldom investigated. This paper aims to develop personalized recommendation techniques to automatically generate mentionees when a user intends to mention others in a post. After analyzing real-world social media datasets we observe that users’ mention behaviors are influenced by not only the semantic but also the spatial context factors of their mentioning activities, which motivate the needs for spatial context-aware user mention behavior modeling. In light of these, we proposed a joint probabilistic model, named Spatial COntext-aware Mention behavior Model (SCOMM), to simulate the process of generating users’ location-tagged mentioning activities. By exploiting the semantic and spatial context factors in a unified way, SCOMM was able to reveal users’ preferences behind their mention behaviors and provide a knowledge model for accurate mentionee recommendations. Furthermore, we designed an Item-Attribute Pruning (IAP) algorithm to overcome the curse of dimensionality and facilitate online top- k query performance. Extensive experiments were conducted on two real-world datasets to evaluate the performance of our methods. The experimental results demonstrated the superiority of our approach by making more effective and efficient recommendations compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Non-preemptive tasks with strict periods are usually adopted in practical multi-core real-time systems when continual sampling and processing of data are required. Systems designers need to provide a proper scheduling strategy such that the tasks' deadlines will be met even under the worst-case conditions. In this paper, we study the scheduling problem of non-preemptive tasks with strict periods in multi-core real-time systems. We first derive a necessary and sufficient condition to determine whether a new task is schedulable upon a multi-core platform without changing the allocations of the existing tasks. Then, with a game theory analogy, we design a recursive method to calculate the maximum permissible execution time for a given task, and propose a new schedulability condition used when the start time and processor assignments of the existing tasks can be modified. Finally, based on the conditions derived previously, we present a task assignment algorithm, which not only provides valid allocations for all tasks, but also obtains the minimum number of processors required by the system. Simulation experiments with randomly generated task sets have been conducted to show the high efficiency and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

A key question for road traffic noise management is whether prediction of human response to road traffic noise could be improved by accounting for noise events instead of, or in addition to, energy equivalent or percentile measures of noise exposure. However, there is a critical prior question: how should noise events in road traffic be measured? Even at moderate traffic flow rates, detecting and counting noise events caused by road traffic is not a trivial exercise, and as yet there is no generally accepted noise event detection algorithm. This paper investigates the performance of a generalized exceedance algorithm for detecting noise events, constructed on the basis of the literature on noise events caused by road traffic. For this purpose, a microscopic traffic simulation model, coupled to an emission model that accounts for distributions of sound power levels of individual vehicles, is used to simulate one-hour time histories of the noise level in the proximity of a roadway, for an exhaustive set of traffic flow/composition and propagation distance conditions in unshielded locations. The validity and reliability of the number of noise events detected by the generalized algorithm in these one-hour time histories is then evaluated for a range of algorithm parameter sets. By discarding parameter sets that do not result in an algorithm that returns valid or reliable counts, and by examining redundancy in the remaining ones, a small number of representative parameter sets is identified, which may prove useful in the construction of event-based indicators supplementary to energy-equivalent measures of road traffic noise. [ABSTRACT FROM AUTHOR]

Manual and automated blind controls are typically not included in energy and daylight simulation in part because there is no consensus in the research or practice communities about the way users operate manual blinds or override automated blinds. In order for blind use patterns to become part of energy and daylight simulation best practices, the range of annual energy and daylighting impacts associated with blind use must be understood. This paper addresses these aspects by comparing four leading candidates of manually-controlled blinds plus two automated blind control algorithms using a high-rise office building located in Boise, ID. This study revealed that all four current “manual” blind use algorithm choices perform relatively similarly to automated systems, and surprisingly sometimes even more efficiently. LM-83 currently has the lowest average occlusion during regularly occupied hours, followed by Lightswitch-2002, while Blindswith-A and -B have the highest average occlusion. The IES-recommended manual blind algorithm resulted even in lower average blind occlusion and lighting energy consumption than automated systems. Finally, life-cycle cost analysis was calculated. The results show that the cost savings from interior automated shading system are substantial over a 30-year time horizon, when compared with common passive manual blinds ($25 versus $7.6 Net Present Value per SF glazing area). [ABSTRACT FROM AUTHOR]

Accurate simulation of photovoltaic characteristics is now a mandatory obligation before validating an experiment; hence, accurate model and parameters of solar cells are indispensable. This paper presents an improved explicit double-diode model based on the Lambert W function (EDDM-LW), and then compares the fitness and parameter extraction performance. By defining two new parameters ( κ and τ ) to separate the exponential function in double-diode model (DDM) and using the Lambert W function, the explicit expression for I-V characteristics is proposed. In contrast to exiting works, the new parameters can readily be computed by the electrical characteristics of the standard test condition without an implicit characteristic. To verify the accuracy of the proposed model, the fitness difference is first investigated with a solar cell and three different types of solar modules. The results indicate that under the same parameter values, EDDM-LW achieves the lowest root mean square error value and exhibits better fitness in representing the I-V characteristics. In addition, the optimal parameters are extracted by an improved teaching-learning-based optimization algorithm. The experimental results show that the optimal parameter values extracted from EDDM-LW are more accurate than those extracted from DDM. Based on these observations, EDDM-LW can be deemed a useful and practical model for the simulation, evaluation, and optimization of the photovoltaic system. [ABSTRACT FROM AUTHOR]

An important open problem in computational complexity theory is to prove the size of circuits, namely, Boolean circuit lower bounds, necessary to solve explicit problems. We survey learning-theoretic approaches to proofs of Boolean circuit lower bounds in this paper. In particular, we discuss how to prove circuit lower bounds in uniform classes by assuming (or constructing) circuit-learning algorithms in several settings, such as the exact, probably approximately correct (PAC), and statistical query learning models. [ABSTRACT FROM AUTHOR]

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently. [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]

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]

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]

This paper applies the Deep Neural Networks to the in-flight parameter identification for detection and characterization of the aircraft icing. General dynamics of the aircraft are firstly presented, ice effects on the dynamics are characterized. Deep Neural Networks (DNNs) including Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are briefly introduced. We propose a “state-image” approach for the pre-processing of the input flight state, then we design a DNN structure which models both local connectivity (using CNN) and temporal characteristics (using RNN) of the flight state. The identified parameters are exported from the DNN output layer directly. To fully evaluate the performance of the DNN-based approach, we conduct simulation tests for different cases which correspond to clean and aircraft icing at different locations (wing, tail, wing and tail) with different severities (moderate, severe). A comparison of the DNN-based approach with a baseline H ∞ -based identification algorithm (state-of-the-art for aircraft icing) is also delivered. Based on the test and comparison results, the DNN-based approach yields more accurate identification performance for more parameters, which shows promising applicability to the in-flight parameter identification problem. [ABSTRACT FROM AUTHOR]

This paper considers scheduling parallelizable jobs in the non-clairvoyant speed scaling setting to minimize the objective of weighted flow time plus energy. Previously, strong lower bounds were shown on this model in the unweighted setting even when the algorithm is given a constant amount of resource augmentation over the optimal solution. However, these lower bounds were given only for certain families of algorithms that do not recognize the parallelizability of alive jobs. In this work, we circumvent previous lower bounds shown and give a scalable algorithm under the natural assumption that the algorithm can know the current parallelizability of a job. When a general power function is considered, this is also the first algorithm that has a constant competitive ratio for the problem using any amount of resource augmentation. [ABSTRACT FROM AUTHOR]

Work of Pezeshki, Calderbank and others [1, 2] has shown that choice of transmission order of Golay complementary waveforms in radar pulse trains can significantly improve Doppler resilience in detecting nonzero Doppler targets. For multiple targets a Weighted average Doppler (WD) algorithm [3] based on our earlier work has been shown to provide improvements. In this paper, we demonstrate that the Weighted Sidelobe Minimization procedure, developed in our recent work that combines the outputs of WD and the existing Binominal Design algorithms for ordering transmitted complementary waveform sequences using a point-wise minimization process (PMP), is also effective for the elimination of false target returns arising from sea clutter. A detailed analysis based on a Swerling II target model in terms of target detection probability is presented to validate the use of PMP. Our validation is strengthened by numerical simulations for both fixed and randomized scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a resource-based task allocation algorithm for multi-robot systems. During mission operations, robots continuously consume their resources which must be refilled during their operations. Unlike other existing auction-based algorithms in which robots do not account for their resources in task allocations, the proposed algorithm considers the resources of the robots to generate their costs. In this algorithm, robots calculate the task performance estimation considering all the possibilities of visiting different combinations of refill stations based on their resource levels. This enables the robots to reduce unnecessary wastage of time and resources during the mission. The effectiveness of the proposed algorithm with respect to task completion time, resource consumption, and communication overhead is theoretically analyzed and is also demonstrated from the simulation of the delivery mission. [ABSTRACT FROM AUTHOR]

Incoherent beam combining (ICBC) technology is one of the most promising ways to achieve high-energy, near-diffraction laser output. In this paper, the momentum method is proposed as a modification of the stochastic parallel gradient descent (SPGD) algorithm. The momentum method can improve the speed of convergence of the combining system efficiently. The analytical method is employed to interpret the principle of the momentum method. Furthermore, the proposed algorithm is testified through simulations as well as experiments. The results of the simulations and the experiments show that the proposed algorithm not only accelerates the speed of the iteration, but also keeps the stability of the combining process. Therefore the feasibility of the proposed algorithm in the beam combining system is testified. [ABSTRACT FROM AUTHOR]

This paper describes four design patterns that aim at systematizing and simplifying the modelling and the implementation of multi-level agent-based simulations. Such simulations are meant to handle entities belonging to different, yet coupled, abstractions or organization levels. The patterns we propose are based on minimal typical situations drawn from the literature. For each pattern, we present use cases, associated data structures and algorithms. For genericity purposes, these patterns rely on a unified description of the capabilities for action and change of the agents. Thus, we propose a precise conceptual and operational framework for the designers of multi-level simulations. [ABSTRACT FROM AUTHOR]

This paper introduces a windowing algorithm for radiation portal monitors based on a transformation of random variables to calculate the ratio between the radiation intensity in two energy windows. Procedures to evaluate critical limits and establish detection limits are described. Simulations and experimental data illustrate a good agreement between derived and observed ratio distributions. The algorithm was compared with commonly-used algorithms. The results illustrate proper evaluation of critical and detection limits even in the case of shadow shielding by vehicles entering a radiation portal monitor. [ABSTRACT FROM AUTHOR]

Semi-Definite Relaxation (SDR) techniques for AC optimal power flow (OPF) have recently been proposed as a means of obtaining a provably global optimal solution for many IEEE benchmark power systems. Solving the resulting semi-definite programs (SDP) can, however, be computationally intensive. Therefore new algorithms and techniques that enable more efficient computations are needed to extend the applicability of SDP based AC OPF algorithms to very large power networks. This paper proposes a three-stage algorithm for AC OPF based storage placement in large power systems. The first step involves network reduction whereby a small equivalent system that approximates the original power network is obtained. The AC OPF problem for this equivalent system is then solved by applying an SDR to the non-convex problem. Finally, the results from the reduced system are transferred to the original system using a set of repeating optimizations. The efficacy of the algorithm is tested through case studies using two IEEE benchmark systems and comparing the solutions obtained to those of DC OPF based storage allocation. The simulation results demonstrate that the proposed algorithm produces more accurate results than the DC OPF based algorithm. [ABSTRACT FROM AUTHOR]

Invulnerability is not the only factor of importance when considering complex networks’ security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent algorithm for fault location on the voltage sourced converter-high voltage direct current (VSC-HVDC) system. This method with single-ended measurement makes full use of frequency, time and energy to capture the fault features via Hilbert-Huang Transform (HHT). The time delay, characteristic frequency, energy attenuation and high-frequency energy are used as the input of ε-support vector regression (SVR) to get fault distance. Then bat algorithm (BA) optimizes the parameters of the model with cross validation comparing with other optimization algorithm. Furthermore, high-frequency variance contribution rate (HVCR) is adopted to identify the fault area. The VSC-HVDC simulation system is constructed to verify the reliability and accuracy of the method and the expected accuracy of the proposed method is ±500 m. The results demonstrated that the proposed method still has reliability and accuracy for hybrid transmission line with a small amount of data. [ABSTRACT FROM AUTHOR]

This paper presents a variable neighborhood search (VNS) optimization algorithm for the design of the parameters of supplementary damping controllers: power system stabilizers (PSS) and the interline power flow controller–power oscillation damping (IPFC–POD) set. The objective is to insert damping to local and inter-area oscillation modes in multimachine power systems. The electric power system dynamics is represented by the current sensitivity model. Simulations were carried out using three systems: a test system known as Two-Area Symmetrical and two real systems, the New England and the Reduced Southern Brazilian. The VNS method was compared with a multi-start algorithm in terms of performance, showing better convergence rates. The proposal was also able to obtain solutions with high damping levels, that showed to be robust when changes on the operating point of the power system were considered. Finally, it has been verified that the PSS controllers were effective for damping the local mode oscillations, while the IPFC–POD set operated mainly damping the inter-area modes. [ABSTRACT FROM AUTHOR]

Holographic image projection on tilted plane has an important application prospect. In this paper, we propose a method to compute the phase-only hologram that can reconstruct a clear image on tilted plane. By adding a constant phase to the target image of the inclined plane, the corresponding light field distribution on the plane that is parallel to the hologram plane is derived through the titled diffraction calculation. Then the phase distribution of the hologram is obtained by the iterative algorithm with amplitude and phase constrain. Simulation and optical experiment are performed to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, convergence capability of the conventional fractional order gradient methods (FOGMs) is analyzed and a new FOGM with guaranteed and faster convergence ability is proposed. In particular, we further consider the case that the derivative order varies between 1 and 2, and give some discussion on how to determine the derivative order in the algorithm settings. Simulation results from a number of numerical examples are provided to illustrate the approaches. [ABSTRACT FROM AUTHOR]

Currently, community detection in complex networks has become a hot-button topic. In this paper, based on the Spectral Clustering (SC) algorithm, we introduce the idea of Jacobi iteration, and then propose a novel algorithm J-SC for community detection in complex networks. Furthermore, the accuracy and efficiency of this algorithm are tested by some representative real-world networks and several computer-generated networks. The experimental results indicate that the J-SC algorithm can accurately and effectively detect the community structure in these networks. Meanwhile, compared with the state-of-the-art community detecting algorithms SC, SOM, K-means, Walktrap and Fastgreedy, the J-SC algorithm has better performance, reflecting that this new algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SOM and Walktrap algorithms. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) is an effective technique that improves the tracking performance of systems by adjusting the feedforward control signal based on the memory data. The key in ILC is to design learning filters with guaranteed convergence and robustness, which usually involves lots of tuning effort especially in high-order ILC. To facilitate this procedure, this paper proposes a systematics approach to design learning filters for arbitrary-order ILC with guaranteed convergence, robustness and ease of tuning. The filter design problem is transformed into an H ∞ optimal control problem for a constructed feedback system. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. The proposed algorithm is further advanced to the one that explicitly considers system variations based on μ synthesis. Important characteristics of the proposed approach such as convergence and robustness are explored and demonstrated through both simulations and experiments on a wafer scanning system. [ABSTRACT FROM AUTHOR]

Recently, the survival signature has been presented as a summary of the structure function which is sufficient for computation of common reliability metrics and has the crucial advantage that it can be applied to systems with components whose failure times are not exchangeable. The survival signature provides a huge reduction in required information, e.g. for its storage, compared to the full structure function, its implementation to larger systems is still difficult in a purely analytical manner and simulations may be required to derive the reliability metrics of interest. Hence, the main question addressed in this paper is whether or not the survival signature provides sufficient information for efficient simulation to derive the system’s failure time distribution. We answer this question in the affirmative by presenting two algorithms for survival signature-based simulation. In addition, we present a third simulation algorithm that can be used in case of repairable components. It turns out that these algorithms are very efficient, beyond the initial advantage of requiring only the survival signature to be available, instead of the full structure function. [ABSTRACT FROM AUTHOR]

Simulations and experiments of laser butt welding are presented in the paper with the laser beam traveling along an inclined line across the gap. Full penetration was observed when the laser beam focused on the gap, while partial penetration was achieved when the laser beam deviated from the gap. A three-dimensional numerical model coupled with a ray tracing algorithm was established to investigate and compare the transient dynamics of the keyhole, molten pool and laser induced plume. The shape of the simulation cross-sections of the welds show good agreement with the experimental results. The gap contributed to transmission of laser beam energy deep into the inner part of the workpiece. The absorbed energy by the workpiece decreased when the laser beam was focused on the gap as some rays escaped through the opening gap. The decrease in absorbed energy resulted in a lower velocity of the molten pool and lower average pressure on the free surface. The velocity and volume of the plume on the top side of the workpiece also decreased. Flow patterns in the molten pool were also identified and discussed. [ABSTRACT FROM AUTHOR]

This paper presents an algebraic based approach and a computational framework for the simulation of multi-axis CNC machining of general freeform tools. The boundary of the swept volume of the tool is precisely modeled by a system of algebraic constraints, using B-spline basis functions. Subdivision-based solvers are then employed to solve these equations, resulting in a topologically guaranteed construction of the swept volume. The presented algebraic-based method readily generalizes to accept tools of arbitrary free-form shape as input, and at the same time, delivers high degree of precision. Being a common representation in CNC simulations, the computed swept volume can be reduced to a dexels’ representation. Several multi-axis test cases are exhibited using an implementation of our algorithm, demonstrating the robustness and efficacy of our approach. [ABSTRACT FROM AUTHOR]

Mathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system’s design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging. In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state-space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information. The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling). [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for optimal management of distributed energy resources in facilities with distributed generation, energy storage systems and specific loads – energy hubs – is shown. This method consists of an iterative algorithm that manages optimal energy flows to obtain the minimum energy cost based on availability of each resource, prices and expected demand. A simulation tool has been developed to run the algorithm under different scenarios. Eight different scenarios of an energy hub have been simulated to illustrate the operation of this method. These scenarios consist of a demand curve under different conditions related to the existence or absence of renewable energy sources and energy storage systems and different electricity tariffs for grid supply. Partial results in the iterative process of the developed algorithm are shown and the results of these simulations are analysed. Results show a good level of optimisation of energy resources by means of optimal use of renewable energy sources and optimal management of energy storage systems. Moreover, the impact of this optimised management on carbon dioxide emissions is analysed. [ABSTRACT FROM AUTHOR]

In this paper, an 8-node hexahedral element based on multi-point integration is proposed in the dynamic explicit framework, with a new adaptive subdivision method adopted. By taking advantages of the assumed strain method, the volumetric and the shear locking of the element are successfully avoided. The locking-free hexahedral element is further applied to coining simulations. Due to large-scale elements and high non-linearity, it's difficult to carry out coining simulations in the context of the implicit framework, especially for a commemorative coin with fine patterns. Thus, an explicit algorithm for coining simulations is developed, which avoids time-consuming iterations and guarantees the computational convergence. Aiming at improving the simulation efficiency furtherly, a multi-layer one-to-four adaptive element subdivision strategy is proposed, with two subdivision criteria with respect to element geometry considered. In this way, the element number in the simulation is dramatically decreased, while the fine patterns of the coin are described exactly. Based on several numerical examples, the locking-free and hourglass-free properties of the proposed hexahedral element are validated, and the accuracy of the coining simulation algorithm is also demonstrated. [ABSTRACT FROM AUTHOR]