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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]

To make agents' route decision-making behaviours as real as possible, this paper proposes a layered navigation algorithm, emphasizing the coordinating of the global route planning at strategic level and the local route planning at tactical level. Specifically, by an improved visibility graph method, the global route is firstly generated based on static environment map. Then, a new local route planning (LRP) based on dynamic local environment is activated for multipath selection to allow pedestrian to respond changes at a real-time sense. In particular, the LRP model is developed on the basis of a passenger's psychological motivation. The pedestrians' individual preferences and the uncertainties existing in the process of evaluation and choice are fully considered. The suitable local path can be generated according to an estimated passing time. The LRP model is applied to the choice of ticket gates at a subway station, and the behaviours of gate choosing and rechoosing are investigated. By utilizing C++, the layered navigation algorithm is implemented. The simulation results exhibit agents' tendency to avoid congestion, which is often observed in real crowds. [ABSTRACT FROM AUTHOR]

Despeckling is a fundamental preprocessing step for applications using polarimetric synthetic aperture radar data in most cases. In this paper, a guided filter with nonlinear weight kernels and adaptive filtering windows is presented for PolSAR image despeckling, in which the guidance image is constructed by a weighted average using the statistical traits of the speckled image. The output result is then estimated by another weighted average, with the aid of the fully polarimetric information from both the guidance image and the speckled image. In the experimental part, the filtering results obtained with both simulated and real PolSAR images reveal the positive performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art algorithms. Furthermore, the relatively low computational complexity is another strength of the proposed method. [ABSTRACT FROM AUTHOR]

The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is generally known for the complexity of its operation. This paper first addresses the challenge of developing an automated on-line near-optimal control strategy for these systems. Particularly, a machine learning logic based on supervised learning is developed for on-line selection of the HEV operating mode, thus the particular set of clutches to be engaged. An efficiency-based approach is then adopted to determine the optimal power-split between powertrain components. Later, the developed strategy finds integration in an optimal design methodology for multimode power-split HEVs considering the effectiveness and ease of on-line controllability. The obtained results are compared with the ones by the traditional HEV design methodology that only considers off-line energy management. The illustrated design methodology with on-line control reveals efficient at identifying the multimode HEV design that demonstrates optimal predicted fuel economy values and ease of on-line controllability simultaneously. Results suggest that the HEV design optimization procedure may produce different outcomes and demonstrate more effective when the evaluation of the on-line controlled operation is integrated. [ABSTRACT FROM AUTHOR]

The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interference-aware online spectrum access problem for multicluster Flying Ad-Hoc Network (FANET) under different network topologies, i.e., the locations of UAV clusters are varying. First, the problem is formulated as a data-assisted multistage channel access game with the goal of mitigating interference of all UAV clusters and decreasing channel switching cost during each slot. As we prove that the game is an exact potential game that guarantees at least one pure-strategy Nash equilibrium and interference-aware online channel preserving based concurrent best response (IOCPCBR) algorithm, an online distributed algorithm is proposed to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of the multistage channel access game as well as IOCPCBR algorithm. [ABSTRACT FROM AUTHOR]

This paper uses an information-theoretic lens to view the error bound of wireless local area network (WLAN) localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment. Interestingly, we analogize the process of WLAN localization into one of information propagation in a parallel Gaussian noisy channel, and then derive the corresponding localization error bound from the channel capacity of the analogical information propagation system. Experimental results show that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed approach performs better in localization error bound estimation under most of the cases of different access point deployment and reference point calibration. [ABSTRACT FROM AUTHOR]

Fast inspection of a defect is a challenging task in mass production of curved surfaces, and photometric stereo (PS) utilizing multiple images from a single camera under a number of different illumination directions is a promising technique for this task due to its high sensitivity to surface normal perturbations. This paper adapts conventional PS and extends the technique to the inspection of non-Lambertian surfaces with high accuracy and efficiency. A data-driven PS is presented by establishing the Gaussian process (GP) model to represent the nonlinear reflectance behavior of various materials based on measured reflectance data sets. With the trained GP model, the surface normal can be estimated in two steps: prediction of bidirectional reflectance distribution function values under different light directions and the subsequent least-squares estimation of a surface normal. Comparison tests with other algorithms on the Mitsubishi Electric Research Laboratories data set and real workpieces with non-Lambertian materials show the superior accuracy and efficiency of the proposed method in surface normal estimation. After the surface normal of the workpiece is recovered, the defects can be detected by filtering out the perturbation of the surface normal. Experiments on steel and glossy polyester workpieces validate the efficacy of the proposed approach in detecting defects on curved non-Lambertian surfaces. [ABSTRACT FROM AUTHOR]

The performance of an oscillator-based read-out circuit for the linear variable differential transformer used as a displacement sensor is studied and various aspects of improving it are presented in this paper. The proposed read-out electronics treats the sensor’s secondary inductance as an unknown reactance, thereby incorporating an active oscillator circuit as a resonator to derive the position information. This method avoids the excitation of the primary coil using low-distortion sine-wave oscillators and the associated phase compensation circuitry as used in traditional signal conditioning circuits. The tradeoffs involved in choosing the frequency counter master clock frequency, the measurement update rate, and the achievable resolution are presented. Various factors that contribute to the nonlinearity have been studied from the standpoint of direct difference versus ratiometric formulas for deriving position information. It is shown that for the oscillator-based read-out procedure, resolutions in the range of 8–16 bits with update rates per channel ranging from 10 Hz to 7.86 kHz, and depending on the procedure used, a worst case full-scale nonlinearity error of ±0.14% are feasible. [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]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given. [ABSTRACT FROM AUTHOR]

The deployment of cloud and edge computing forms a three-tier mobile computing network, where each task can be processed locally, by the edge nodes, or by the remote cloud server. In this paper, we consider a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes. In this network, we jointly optimize the offloading decision and the computation resource allocation to minimize the average task duration subject to the limited battery capacity of devices. However, the formulated problem is a large-scale mixed integer non-linear optimization problem with the growing number of base stations and devices, which is NP-hard in general. To develop an efficient offloading scheme with low complexity, we conduct a series of reformulation based on reformulation linearization technology and further propose a parallel optimization framework by utilizing alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. The proposed scheme decomposes the large-scale problem into some smaller subproblems, which are done across the multiple computation units in a parallel fashion to speed up the computation process. Simulation results demonstrate that the proposed scheme can obtain a near-optimal performance with low complexity, and can reduce up to 24% of the task duration compared with other schemes. Simulation also shows how much the vertical and horizontal computation cooperations affect the system performance under different network parameters. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep reinforcement learning (DRL)-based method that allows unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex environments. This technique is important for many applications such as goods delivery and remote surveillance. The problem is formulated as a partially observable Markov decision process (POMDP) and solved by a novel online DRL algorithm designed based on two strictly proved policy gradient theorems within the actor-critic framework. In contrast to conventional simultaneous localization and mapping-based or sensing and avoidance-based approaches, our method directly maps UAVs’ raw sensory measurements into control signals for navigation. Experiment results demonstrate that our method can enable UAVs to autonomously perform navigation in a virtual large-scale complex environment and can be generalized to more complex, larger-scale, and three-dimensional environments. Besides, the proposed online DRL algorithm addressing POMDPs outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Haul fleet size determination is a critical task in any surface mining operation where the material is handled using the truck-and-shovel system. Although the problem of finding the optimum haulage fleet size has been thoroughly studied, there are two important shortcomings: disregarding the effects of downstream processes on the operation and ignoring the fleet management system effects. This paper presents an integrated simulation-optimization framework to address the haul fleet size determination problem surface mines and target the two shortcomings listed above. In the developed framework, the mining operation, the processing plants, and the operational decision tools communicate with each other to find the best size of the haul fleet required to meet the production schedule. Results of the study show that the developed framework is capable of handling the operation with 13% less number of trucks than the required number of trucks suggested by deterministic calculations. [ABSTRACT FROM AUTHOR]

One of the most important concerns in power system networks is harmonics, which is mainly developed with the huge usage of nonlinear loads and power electronic equipment. This harmonics leads to damage in the distribution system. This paper proposes an efficient hybrid approach with SHAPF for harmonic mitigation to maintain the PQ in the distribution system. An efficient hybrid approach is the joined execution of an EGOA and ANN named as EGOANN. The main aim of the proposed strategy is to improve the quality of power by mitigating the harmonics subject to the minimum THD. In the proposed approach, EGOA optimizes the dataset of fundamental and harmonic loop parameters such as Terminal voltage and DC voltage present in the SHAPF based on the load change and parameter variation of the system. In light of the accomplished dataset, ANN accurately predicts the parameters and produces optimized control signals. Finally, the proposed model is executed in the MATLAB/Simulink working platform, also includes the simulation results of a few test cases to maintain the PQ, and illustrates the superior performance of the proposed design philosophy. The simulation result shows that the harmonic distortion can be reduced to an acceptable level in response to the load change or parameter variation of the power system by the proposed EGOANN hybrid technique. Also, the proposed technique is more effective with less computation and reduces the complexity of the algorithm. [ABSTRACT FROM AUTHOR]

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application. [ABSTRACT FROM AUTHOR]

Abstract Many codes have been developed to study highly relativistic, magnetized flows around and inside compact objects. Depending on the adopted formalism, some of these codes evolve the vector potential A , and others evolve the magnetic field B = ∇ × A directly. Given that these codes possess unique strengths, sometimes it is desirable to start a simulation using a code that evolves B and complete it using a code that evolves A. Thus transferring the data from one code to another would require an inverse curl algorithm. This paper describes two new inverse curl techniques in the context of Cartesian numerical grids: a cell-by-cell method, which scales approximately linearly with the numerical grid, and a global linear algebra approach, which has worse scaling properties but is generally more robust (e.g., in the context of a magnetic field possessing some nonzero divergence). We demonstrate these algorithms successfully generate smooth vector potential configurations in challenging special and general relativistic contexts. Highlights • Our solution consists of a numerical implementation of the "inverse curl" operator. • We use two different methods that have different strengths and weaknesses. • The cell-by-cell technique is very fast and scales with the number of gridpoints. • The global linear algebra method is slower but has better symmetry properties. [ABSTRACT FROM AUTHOR]

In the traditional EKF-SLAM algorithm, the computational complexity and uncertainty will grow up rapidly with the increase of the feature points and the enlargement of the map coverage. As we know, the computational complexity is proportional to the quadratic of the number of feature points contained in a single filtering process. The approach represented in the paper combines EKF-SLAM with local submaps, which can improve the computational efficiency and reduce the computational complexity. At first, an independent local submap is established for the observed feature points. When the number of feature points contained in the local submap reaches a certain threshold value, the local submap is integrated into the global map. At last, the submap is initialized again. The simulation results show that the approach can reduce the computational complexity effectively and increase the computation speed greatly in the case of maintaining the computational accuracy of the traditional EKF-SLAM algorithm. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the problem of adaptive trajectory tracking control for a class of nonlinear systems with structured parametric uncertainties. We propose to use an iterative modular approach: we first design a robust nonlinear state feedback that renders the closed‐loop input‐to‐state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed‐loop output tracking error. Next, we propose an iterative adaptive algorithm, where we augment this robust ISS controller with an iterative data‐driven learning algorithm to estimate online the parametric uncertainties of the model. We implement this method with two different learning approaches. The first one is a data‐driven multiparametric extremum seeking method, which guarantees local convergence results, and the second is a Bayesian optimization‐based method called Gaussian Process Upper Confidence Bound, which guarantees global results in a compact search set. The combination of the ISS feedback and the data‐driven learning algorithms gives a learning‐based modular indirect adaptive controller. We show the efficiency of this approach on a two‐link robot manipulator numerical example. [ABSTRACT FROM AUTHOR]

Summary: One of the most important steps in designing a model predictive control strategy is selecting appropriate parameters for the relative weights of the objective function. Typically, these are selected through trial and error to meet the desired performance. In this paper, a reinforcement learning technique called learning automata is used to select appropriate parameters for the controller of a differential drive robot through a simulation process. Results of the simulation show that the parameters always converge, although to different values. A controller chosen by the learning process is then ported to a real platform. The selected controller is shown to control the robot better than a standard model predictive control. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive synthesis using the particle swarm optimization (PSO) for implementations of resonator-coupled filters is proposed. The coupling matrix of in-band filtering response is achieved and optimized by the PSO-based synthesis. Meanwhile, the stopband coupling matrix of the filter is predicted based on the combination of extra resonant nodes representing stopband spurious and parasitic effect of input/output ports. In addition, an enhanced accurate prediction of filter performance is calculated by the proposed approach, considering the practical fabrication tolerance on filter design. To verify principles mentioned earlier, various resonator-coupled filters are implemented. The calculation, EM-simulation, and measurement of filters show good agreements in both passband and stopband. [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]

We describe in this paper the experimental procedure, the data treatment and the quantification of the black body correction: an experimental approach to compensate for scattering and systematic biases in quantitative neutron imaging based on experimental data. The correction algorithm is based on two steps; estimation of the scattering component and correction using an enhanced normalization formula. The method incorporates correction terms into the image normalization procedure, which usually only includes open beam and dark current images (open beam correction). Our aim is to show its efficiency and reproducibility: we detail the data treatment procedures and quantitatively investigate the effect of the correction. Its implementation is included within the open source CT reconstruction software MuhRec. The performance of the proposed algorithm is demonstrated using simulated and experimental CT datasets acquired at the ICON and NEUTRA beamlines at the Paul Scherrer Institut. [ABSTRACT FROM AUTHOR]

In this paper, a novel prediction method for low carbon steel is proposed based on an improved twin support vector regression algorithm. 300 qualified samples are collected by the sublance measurements from the real plant. The simulation results show that the prediction models can achieve a hit rate of 96% for carbon content within the error bound of 0,005% and 94% for temperature within the error bound of 15 °C. The double hit rate reaches to 90%. It indicates that the proposed method can provide a significant reference for real BOF applications, and also it can be extended to the prediction of other metallurgical industries. [ABSTRACT FROM AUTHOR]

Abstract In cylindrical/spherical geometries, MHD solvers using explicit finite-volume methods face restrictive time steps imposed by the CFL condition due to the clustering of cells in the azimuthal direction near the pole/axis. We use a conservative averaging-reconstruction method (Ring Average) on structured cylindrical/spherical mesh to remove this severe time step restriction for multi-dimensional curvilinear finite-volume MHD solvers. The Ring Average technique is implemented as a post-processing step and thus requires no changes to the existing data structure, grid definition or numerical methods in the original MHD solver. This paper describes the Ring Average algorithm and presents simulation results using field loop advection and MHD blast waves in cylindrical/spherical geometries for validation. The algorithm is shown to be inexpensive and easily implemented in existing curvilinear finite-volume MHD solvers. Highlights • Ring Average is a "post-processing" module without modifying the original solver. • Ring Average relaxes time-step restrictions in MHD solvers with axis singularities. • Ring Average conserves mass, momentum and energy for proper shock propagations. • Ring Average works for codes with either divergence cleaning or constrained transport. [ABSTRACT FROM AUTHOR]

Network-on-chip (NoC) mapping algorithms significantly affect NoC system performance in terms of communication cost and energy consumption. For a specific application represented by a task graph, this paper proposes an energy-efficient mapping algorithm that searches for the mapping decision with best communication locality and therefore lowest energy consumption. To this end, we formulate the concerned mapping problem as an optimization model, and propose an effective meta-heuristic algorithm to solve the formulated optimization model. During the mapping procedure, we employ a simulation-free, communication probability-based energy model to evaluate the quality of each candidate mapping. By iteratively updating the best explored mapping decision using a meta-heuristic search strategy, the mapping procedure can eventually identify an mapping decision with optimal energy efficiency in the search space. The proposed mapping algorithm has been verified on NoC systems of different sizes using a variety of benchmark applications. Simulation results demonstrate that the mapping decision produced by this algorithm achieves an up to 23% energy reduction compared with the traditional round-robin strategy. [ABSTRACT FROM AUTHOR]

Among traditional Light Detection And Ranging (LIDAR) data representations such as raster grid, triangulated irregular network, point clouds and octree, the explicit 3D nature of voxel-based representation makes it a promising alternative. Despite the benefit of voxel-based representation, voxel-based algorithms have rarely been used for building detection. In this paper, a voxel segmentation-based 3D building detection algorithm is developed for separating building and nonbuilding voxels. The proposed algorithm first voxelizes the LIDAR point cloud into a grayscale voxel structure in which the grayscale of the voxel corresponds to the quantized mean intensity of the LIDAR points within the voxel. The voxelized dataset is segmented into multiple 3D-connected regions depending on the connectivity and grayscale similarity among voxels. The 3D-connected regions corresponding to the building roof and facade are detected sequentially according to characteristics such as their area, density, elevation difference and location. The obtained results for the detected buildings are evaluated by the LIDAR data provided by working group III/4 of ISPRS, which demonstrate a high rate of success. Average completeness, correctness, quality, and kappa coefficient indexes values of 90.0%, 96.0%, 88.1% and 88.7%, respectively, are obtained for buildings. [ABSTRACT FROM AUTHOR]

The current marketing landscape, apart from conventional approaches, consists of campaigns designed especially for launching information diffusion processes within online networks. Associated research is focused on information propagation models, campaign initialization strategies and factors affecting campaign dynamics. In terms of algorithms and performance evaluation, the final coverage represented by the fraction of activated nodes within a target network is usually used. It is not necessarily consistent with the real marketing campaigns using various characteristics and parameters related to coverage, costs, behavioral patterns and time factors for overall evaluation. This paper presents assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations. The results, which are delivered from a simulation model based on synthetic networks in a form of decision scenarios, are verified within a real network. Last, but not least, the study proposes a multi-objective campaign evaluation framework with several campaign evaluation metrics integrated. The results showed that the recommendations generated with the use of synthetic networks applied to real networks delivered results according to the decision makers’ expectation in terms of the used evaluation criteria. Apart from practical applications, the proposed multi-objective approach creates new evaluation possibilities for theoretical studies focused on information spreading processes within complex networks. [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 • Two indexes are considered, which are number of consumed resources R and distance to boundary I. Minimizing R and minimizing I can reduce blocking probability of network. • Optimizations of R and I are sometimes contradictory, so a RMSA algorithm is proposed, which can make a reasonable tradeoff between these two indexes. • Blocking probability of the proposed algorithm is significantly lowered, comparing with the existing well-performed RMSA algorithms. Abstract Elastic optical network (EON) has been a promising technology for the next generation of core transport networks, because it can support flexible spectrum assignment. Routing, modulation and spectrum assignment (RMSA) is one of the key issues in EON. In this paper, two indexes are considered during the spectrum assignment process, number of consumed resources R and distance to boundary I. Minimizing R means using the least resources to establish a connection while minimizing I implies that remainder idle resources are more lumped after a connection is established. Both of them can reduce blocking probability of network. However, optimizations of R and I are sometimes contradictory, so we propose three policies to make a tradeoff between them, which are R : I , I : R and R + I. The R : I policy takes R as the primary objective while the I : R policy sets I as the chief one; the R + I policy takes a linear combination α R + (1 - α) I as the optimizing objective. Simulations are taken on the NSF-Net and UBN topologies, which show that the R + I policy can get the best performance in most cases. We further compare the R + I policy with two existing well-performed algorithms, the First-Last-Fit algorithm and the Block-Assignment algorithm. Simulation results reveal that blocking probability of the R + I policy is far less than that of the comparative objects. [ABSTRACT FROM AUTHOR]

Highlights • Improving the performance of optical packet switches (OPS) is challenging. • No single wavelength conversion (WC) technology is able to improve packet loss. • A hybrid OPS architecture with limited-range and parametric WCs is proposed. • Proposed OPS reduces packet loss significantly compared to known OPS architectures. Abstract In this paper, we propose and study the performance of two conversion schemes for optical packet switch (OPS) with the objective of reducing packet contention, and hence, improving the overall packet loss ratio (PLR). In both schemes, conversion is performed in two layers. The first layer contains a dedicated wavelength converter (WC) for each input wavelength, while the second conversion layer consists of a dynamic pump-wavelength selection (DPS) of parametric wavelength converters (PWCs) shared in a Feed-Back fashion. The first scheme uses non-circular WCs (NCWCs) with DPS of chained PWCs (called LDPSC, for short). In the LDPSC scheme, an incoming packet is accepted only if it could be assigned to its required output using only the NCWCs in the first layer or more than one PWCs in a chained fashion in the second layer; otherwise it will be dropped. The second scheme uses circular wavelength conversion (CWCs) with DPS of PWCs without chaining (named CDPS, for short). In the CDPS scheme, an incoming packet is accepted only if it can be assigned to its required output using only CWCs in the first layer or only one of the PWCs in the second layer; otherwise it will be dropped. For each of the two schemes, a matching algorithm along with a Dynamic pump conversion (DPC) algorithm is presented. The performances of the proposed schemes are compared to two conventional schemes; namely, the DPSC scheme that uses a shared pool of DPS of chained PWCs, and the LDPS scheme that uses a dedicated NCWC for each input wavelength and a shared pool of DPS of PWCs without chaining. Simulation results show that, the new CDPS scheme achieves the same performance (in terms of PLR) of the LDPS scheme, while reducing the required number of PWCs by more than 44 % and 57 % for conversion distances (that is the maximum number of wavelengths adjacent to any input wavelength on any one of its sides), d = 1 and 2; respectively. Moreover, using conversion distance d = 1 , the new LDPSC scheme reduces the PLR compared to the LDPS scheme by up to 85 % and 22 % for traffic loads, 0.5 and 1; respectively. In addition, for d = 1 , the new LDPSC scheme reduces the PLR compared to the DPSC scheme by up to 99.8 % and 30 % for traffic loads, 0.5 and 1; respectively. [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 • 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]

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]

Force identification is a crucial inverse problem in structural dynamics. In this paper, a new method integrating Taylor formula algorithm and acceleration transmissibility concept is put forward to identify the magnitude and location of loads in time domain. The Taylor formula algorithm expresses the response vectors as Taylor-series expansion and then, a series of deductions are implemented. Ultimately an explicit discrete equation which associates output acceleration response, structure characteristic and input excitation together is established. After establishing the explicit discrete equation, acceleration transmissibility concept is utilized to identify the location of forces. Under the premise of knowing the acceleration response and structure characteristic, force magnitude can be calculated. To verify the effectiveness of proposed method, one builds up a theoretical simulation model in which different types of dynamic excitations are exerted on an inflatable cantilever beam. Meanwhile, classical state space algorithm is made a contrast with Taylor formula algorithm in the step of force magnitude identification. Calculation results demonstrate that the integration method is capable of identifying the location of excitations precisely. Reconstruction of force time history reaches a higher accuracy compared to state space algorithm as well. In addition, the anti-noise ability of proposed algorithm is discussed. [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]

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]

Purpose This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime.Design/methodology/approach This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm.Findings Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime.Research limitations/implications In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only.Practical implications The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations.Social implications The research highly gains impacts toward mobile-based applications.Originality/value A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs. [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]

Publication bias can significantly limit the validity of meta-analysis when trying to draw conclusion about a research question from independent studies. Most research on detection and correction for publication bias in meta-analysis focus mainly on funnel plot-based methodologies or selection models. In this paper, we formulate publication bias as a truncated distribution problem, and propose new parametric solutions. We develop methodologies of estimating the underlying overall effect size and the severity of publication bias. We distinguish the two major situations, in which publication bias may be induced by: (1) small effect size or (2) large p-value. We consider both fixed and random effects models, and derive estimators for the overall mean and the truncation proportion. These estimators will be obtained using maximum likelihood estimation and method of moments under fixed- and random-effects models, respectively. We carried out extensive simulation studies to evaluate the performance of our methodology, and to compare with the non-parametric Trim and Fill method based on funnel plot. We find that our methods based on truncated normal distribution perform consistently well, both in detecting and correcting publication bias under various situations. [ABSTRACT FROM AUTHOR]

Centroidal Voronoi tessellation (CVT)-based mesh generation is a very effective technique for creating high-quality Voronoi meshes and their dual Delaunay triangulations that often play a crucial role in applications, including ocean and atmospheric simulations using finite volume schemes. In the next generation climate models, the spacing scales change dramatically across the whole sphere and require ultra-high resolution and smooth transitions from coarse to fine grid regions. Thus fast and robust spherical CVT (SCVT) meshing algorithms become highly desirable. In this paper, we first propose a Lloyd-preconditioned limited-memory BFGS method for constructing SCVTs that is also applicable to the construction of CVTs of general domains. This method is then parallelized based on overlapping domain decomposition, enabling excellent scalability on distributed systems. Results of several computational experiments show that the new method could incur computational time costs one order of magnitude smaller compared with some existing methods for generating large-scale highly variable-resolution meshes, while also providing significant improvements in mesh quality. [ABSTRACT FROM AUTHOR]

The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range. [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]

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]

Oil reservoir history matching is a well-known inverse problem for predicting production by optimizing enormous unknown parameters with numerical simulation. Typically it can be formulated in a Bayesian framework with geological priors. Instead of gradient-based optimization with the possibility of converging to a local minimum, evolutionary algorithms have been introduced to globally find optimal parameters. Due to the high-dimensional parameters, the optimization could become inefficient; therefore, many dimensionality reduction algorithms have been applied in history matching. However, these methods suffer from the linear assumption or the pre-image problem, which could affect the model optimization. In this paper, based on the evolutionary algorithm termed Multi-objective Evolutionary Algorithm Based on Decomposition, which is capable of simultaneously optimizing the parameters with respect to the data of several oil wells, we propose history matching with dimensionality reduction by explicitly utilizing the nonlinear dimensionality reduction model Auto-Encoder to reduce the number of unknown parameters, which can naturally handle the pre-image problem and then improve model performance in terms of precision and complexity. Experimental results based on PUNQ-S3 data verify the efficiency of the newly proposed methods. [ABSTRACT FROM AUTHOR]

Time Petri nets (TPNs) have been widely used for modeling discrete event systems such as manufacturing, supply chain, and military systems. However, TPNs still have many drawbacks in some scenarios where an operation or process is associated with probability, and also lack appropriate simulation algorithms for analyzing different types of systems. In this paper, we address these two issues by proposing a class of extended time Petri nets (ETPNs) and presenting an appropriate simulation algorithm. We illustrate and validate our approach using a hypothetic command and control system, which shows that this approach could be a powerful tool for modeling and analyzing discrete event systems. [ABSTRACT FROM AUTHOR]

An efficient molecular dynamics (MD) algorithm is presented in this paper for biomolecular systems, which incorporates a novel variation on the fast multipole method (FMM) coupled to the reversible reference system propagator algorithm (r-RESPA). A top-down FMM is proposed which calculates multipoles recursively from the top of the box tree instead of from the bottom in Greengard’s original FMM, in an effort to be more efficient for noncubic or nonuniform systems. In addition, the use of noncubic box subdivisions of biomolecular systems is used and discussed. Reversible RESPA based on a Trotter factorization of the Liouville propagator in generating numerical integration schemes is coupled to the top-down FMM and applied to a MD study of proteins in vacuo, and is shown to be able to use a much larger time-step than the standard velocity Verlet method for a comparable level of accuracy. Furthermore, by using the FMM it becomes possible to perform MD simulations for very large biomolecules, since memory and CPU time requirements are now nearly of order of O(N) instead of O(N2). For a protein with 9513 atoms (the photosynthetic reaction center), the efficient MD algorithm leads to 20-fold reduction in CPU time for the Coulomb interaction and approximately 15-fold reduction in total CPU time over the standard velocity Verlet algorithm with a direct evaluation of Coulomb forces. © 1995 American Institute of Physics. [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]

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient. [ABSTRACT FROM AUTHOR]