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Recently the distributed estimation problem with communication constraints has been widely studied for sensor network application. Our work focus on the diffusion Kalman filter with communication constraints. To satisfy finite communication resources constraints, this paper presents a multi-channel decoupled event-triggered strategy which improves the utilization of the network communication resources. With this strategy, only some entries of sensors’ measurements are transmitted if their triggering criteria are satisfied. We apply this strategy to the step 1 of the diffusion Kalman filter and analyze its performance. The analysis shows that the multi-channel decoupled event-triggered diffusion Kalman filter is unbiased in mean sense and is convergent in mean-square sense. The theoretical steady-state mean-square deviation (MSD) and communication cost are also given in this article. Simulation results demonstrate a good match between the theory analysis and experiment. Finally this algorithm is applied to the optic-electric sensor network, and the results verify the effectiveness of the proposed strategy in terms of the communication resources utilization. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing has become an emerging computing and sensing paradigm that recruits ordinary participants to perform sensing tasks. With the highly dynamic mobility pattern and the abundance of on-board resources, vehicles have been increasingly recruited to participate large-scale crowd sensing applications such as urban sensing. However, existing participant recruitment algorithms take a long time in recruitment decision for large number of vehicular participants. In this paper, a fast algorithm for vehicle participant recruitment problem is proposed, which achieves linear-time complexity at the sacrifice of a slightly lower sensing quality. The participant recruitment problem is modeled as a unconstrained maximization problem without explicitly cost constraint and a trade-off parameter is introduced to control the recruiter cost. Trace-driven simulations on both real-world and synthetic data-sets are conducted to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm is 50 times faster than the state-of-art algorithm at the sacrifice of 5 % lower sensing quality when the number of participants is over 1000. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of an algorithm for a structure-based infill patterning system for big-scale 3D printed parts. A simple, modular, expandable system is presented that considers the structural logic of printable parts through the voxelization of 3D virtual models into a finite element representation that is used to feed a spring model. Material properties and user customization are discussed. Several ad-hoc solutions, optimization capabilities and simulation results are tested and shown. The use of non-standard, customized nozzle designs and print materials is discussed for further usage capabilities. [ABSTRACT FROM AUTHOR]

The high efficiency video coding (HEVC) standard shows enhanced video compression efficiency at the cost of high performance requirements. To address these requirements different approaches, like algorithmic optimization, parallelization and hardware acceleration can be used leading to a complex design space. In order to find an efficient solution, early design verification and performance evaluation is crucial. Hereby the prevailing methodology is the simulation of the complex HW/SW architecture. Targeting heterogeneous designs, different simulation models have different performance evaluation capabilities making a combined HW/SW co-analysis of the entire system a cumbersome task. To facilitate this co-analysis, we propose a non-intrusive instrumentation methodology for simulation models, which automatically adapts to the model under observation. With the help of this instrumentation methodology we perform the analysis and exploration of different design aspects of a SystemC-based heterogeneous multi-core model of an HEVC intra encoder. In the course of this HW/SW co-analysis various aspects of the parallelization and hardware acceleration of the video coding algorithms are presented and further improved. Due to its cycle accurate nature the developed model is well suited to facilitate various performance evaluations and to drive HW/SW co-optimizations of the explored system, as discussed in this paper. [ABSTRACT FROM AUTHOR]

Identifying the most influential spreaders in complex networks is crucial for optimally using the network structure and designing efficient strategies to accelerate information dissemination or prevent epidemic outbreaks. In this paper, by taking into account the centrality of a node and its neighbors’ centrality which depends on the diffusion importance of links, we propose a novel influence measure, the weight neighborhood centrality, to quantify the spreading ability of nodes in complex networks. To evaluate the performance of our method, we use the Susceptible–Infected–Recovered (SIR) model to simulate the epidemic spreading process on six real-world networks and four artificial networks. By measuring the rank imprecision and the rank correlation between the rank lists generated by simulation results via SIR and the ones generated by centrality measures, it shows that in general the weight neighborhood centrality can rank the spreading ability of nodes more accurately than its benchmark centrality, especially when using the degree k or coreness k s as the benchmark centrality. Further, we compare the monotonicity and the computational complexity of different ranking methods, which show that our method not only can be better at distinguishing the spreading ability of nodes but also can be used in large-scale networks due to the high computation efficiency. [ABSTRACT FROM AUTHOR]

YouTube is currently the most popular and successful video sharing website. The videos on YouTube have become a treasure of data, which can be used in various fields of research ranging from STEM education to Medical science. However, all the previous research, studies, and analysis so far, are only conducted on very small volume of YouTube video data. To date, no mechanism exists to systematically and continuously collect, process and store the rich set of YouTube data. In this paper, we present a methodology to fill the gap, i.e., systematically and continuously mine and store the YouTube data. The methodology has two modules, a video discovery and a video meta-data collection. Our methodology is robust, efficient and scalable. Over the period of two months, using our methodology, we discovered 16,000,000 videos and mined the complete meta-data of more than 42,000 videos. [ABSTRACT FROM AUTHOR]

Geographic protocols have been recently developed for data routing in wireless ad hoc networks. These protocols are based on the exact location of each sensor node, by assuming that sensor nodes are equipped with geographic positioning services or localization-based techniques. At each node, the routing decision is based only on the position of the destination node and the position of the forwarding node’s neighbors. Location-based techniques, such DV-Hop, have attracted a lot of interest in order to estimate nodes positions. In this paper, we evaluate the performance of DV-Hop localization technique for estimating nodes localization and integrate it within the greedy perimeter stateless routing protocol. Simulations have been conducted to show the effectiveness of using localization-based techniques when integrated into geographical routing protocols for wireless sensor networks. [ABSTRACT FROM AUTHOR]

Subset simulation is an efficient Monte Carlo technique originally developed for structural reliability problems, and further modified to solve single-objective optimization problems based on the idea that an extreme event (optimization problem) can be considered as a rare event (reliability problem). In this paper subset simulation is extended to solve multi-objective optimization problems by taking advantages of Markov Chain Monte Carlo and a simple evolutionary strategy. In the optimization process, a non-dominated sorting algorithm is introduced to judge the priority of each sample and handle the constraints. To improve the diversification of samples, a reordering strategy is proposed. A Pareto set can be generated after limited iterations by combining the two sorting algorithms together. Eight numerical multi-objective optimization benchmark problems are solved to demonstrate the efficiency and robustness of the proposed algorithm. A parametric study on the sample size in a simulation level and the proportion of seed samples is performed to investigate the performance of the proposed algorithm. Comparisons are made with three existing algorithms. Finally, the proposed algorithm is applied to the conceptual design optimization of a civil jet. [ABSTRACT FROM AUTHOR]

This paper deals with the development of methodology suited for design of computation algorithm which is able to determine power losses of electronic systems based on measured temperature distribution (thermo-vision measurements). For such purpose deterministic algorithms for power loss identification were developed using two different approaches. Both of them were verified by simulation experiments and relevant simulation models. Applications of proposed methodologies were further used for more complex electronic systems – brick power supply module. First, thermo-vision measurements of selected system's operation were done in order to verify proposed estimation methodologies. The power loss determination estimated from designed methodologies was verified by the comparison of results from the thermo-vision measurements of operation and the experiments of thermal simulation. Finally, the suitability of methodologies was tested in more complex systems such as smartphones. The investigation of accuracy of the designed methodology was performed similarly by the comparison of simulation and measurements. [ABSTRACT FROM AUTHOR]

This paper presents the problem of using simulation modelling - both with a view of assessing the efficiency of online service systems control and improving such efficiency by applying simulation models within the framework of the corresponding control algorithms. In the first case, the model is used in emulation mode when it is used as a tool for creating various situations in servicing environment which interacts with the already used algorithms of dispatching and control. In the second case, the model is used in the real-time mode when it is used as a tool for checking candidate solutions to be taken in a specific on-the-spot situation. A review of literature is presented showing the experience of using simulation models both in emulation mode and in the mode of tracking real-time processes. [ABSTRACT FROM AUTHOR]

Aiming to co-channel interference (CCI) for LTE downlink multi-user MIMO-OFDM system, maximizing the successive signal to leakage plus noise ratio (SSLNR) interference cancellation algorithm based on Pre-processing is proposed. The algorithm utilizes SSLNR based on precoding matrix scheme to process emission signal and the known leakages is canceled by dirty paper coding (DPC) algorithm at the transmitter. At the receiver, the received signal are treated by preconditioned matrix, the interference between different users are eliminated in further, the sum-capacity is improved by pre-processing Matrix. Meanwhile, the algorithm is not limited by the number of transmit and receive antennas. The performance improvement is verified by simulation, when BER is 10 -4 , signal noise ratio (SNR) is improved about 4dB. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive Kalman filter algorithm is proposed to cope with the unknown a priori covariance matrix of process noise for the linear discrete-time systems. The process noise covariance matrix is estimated by the proposed algorithm based on the measurement sequence. Accordingly, we construct a new measurement sequence to sequentially estimate process covariance matrix in terms of the relationship between the measurement and process noise sequence. Then the stability of the proposed algorithm is analyzed. The algorithm shows a simple recursive form and great performance enhancement of application. Finally, the navigation simulation results are presented to illustrate the validity and practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In complex networks, information regarding the nodes is usually incomplete because of the effects of interference, noise, and other factors. This results in parts of the network being blurred and some information having an unknown source. In this paper, a spectral clustering algorithm is used to identify fuzzy nodes and solve network reconstruction problems. By changing the fuzzy degree of placeholders, we achieve various degrees of credibility and accuracy for the restored network. Our approach is verified by experiments using open source datasets and simulated data. [ABSTRACT FROM AUTHOR]

Electrochemical impedance spectroscopy (EIS) is of great value to corrosion studies because it is sensitive to transient changes that occur in the metal-electrolyte interface. A useful way to link the results of electrochemical impedance spectroscopy to corrosion phenomena is by simulating equivalent circuits. Equivalent circuit models are very attractive because of their relative simplicity, enabling the monitoring of electrochemical systems that have a complex physical mechanism. In this paper, the stochastic algorithm Differential Evolution is proposed to fit an equivalent circuit to the EIS results for a wide potential range. EIS is often limited to the corrosion potential despite being widely used. This greatly hinders the analysis regarding the effect of the applied potential, which strongly affects the interface, as shown, for example, in polarization curves. Moreover, the data from both the EIS and the DC values were used in the proposed scheme, allowing the best fit of the model parameters. The approach was compared to the standard Simplex square residual minimization of EIS data. In order to manage the large amount of generated data, the EIS-Mapper software package, which also plots the 2D/3D diagrams with potential, was used to fit the equivalent circuit of multiple diagrams. Furthermore, EIS-Mapper also computed all simulations. The results of 67 impedance diagrams of stainless steel in a 3.5% NaCl medium at 25 °C obtained in steps of 10 mV, and the respective values of the fitted parameters of the equivalent circuit are reported. The present approach conveys new insight to the use of electrochemical impedance and bridges the gap between polarization curves and equivalent electrical circuits. [ABSTRACT FROM AUTHOR]

The area of projected shadow on building facades may play a role of paramount importance in the prediction of solar energy conversion and building thermal and energy performance. Algorithms for solar shading calculations may directly affect the accuracy of results obtained by building energy simulation tools. In addition, the consideration of uncertain design parameters imply an estimative of a confidence interval of solar fraction results that determine the reliability as well as the improvement of shading calculation models. In this way, this paper presents a methodology for uncertainty and sensitivity analysis applied to the prediction of sunlit area on building exterior surfaces with shading devices. From a single building model, the solar fraction was investigated for two cities and four typical days. According to the obtained results, more than 20% of all simulation time steps have uncertainties equal or higher than 5% on the solar fraction calculation at 95% confidence level. The maximum values of the uncertainties occur in warmer months, which could significantly impact the building energy balance. Three inputs have higher influence on the uncertainties on solar fraction results: (1) latitude, (2) orientation and (3) width of overhang fins. Perturbation of only 5° on the building orientation may imply important uncertainties on solar fraction, which is normally neglected by designers. The results highlight the importance of assessing the uncertainty of inputs in algorithms such as the one to calculate the surface solar fraction that is an important prerequisite of the final building energy balance. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm to choose a good partition in bipartite networks has been proposed. Bipartite networks have more theoretical significance and broader prospect of application. In view of distinctive structure of bipartite networks, in our method, two parameters are defined to show the relationships between the same type nodes and heterogeneous nodes respectively. Moreover, our algorithm employs a new method of finding and expanding the core communities in bipartite networks. Two kinds of nodes are handled separately and merged, and then the sub-communities are obtained. After that, objective communities will be found according to the merging rule. The proposed algorithm has been simulated in real-world networks and artificial networks, and the result verifies the accuracy and reliability of the parameters on intimacy for our algorithm. Eventually, comparisons with similar algorithms depict that the proposed algorithm has better performance. [ABSTRACT FROM AUTHOR]

Extinction coefficient is the base parameter of visibility measurement, its measurement error determines the accuracy of the visibility meter. In the paper, the error sources are analyzed, and a new optical structure is proposed by adding the optical compensator. For the new structure, the measurement model based on boundary constraints is provided to reduce measure errors. Based on the new model, use an improved algorithm to solve the error convergence problem. The new algorithm use exponent arctangent function as base function, three parameters i, j and k control the contour of the function. With simulation tool, the optimization values are obtained. The experimental results show that the extinction coefficient relative error is reduced by 18% in the entire measurement region, the fluctuations of the relative error is reduced to 7.3%. [ABSTRACT FROM AUTHOR]

A promising framework that describes traffic conditions in urban networks is the macroscopic fundamental diagram (MFD), relating average flow and average density in a relatively homogeneous urban network. It has been shown that the MFD can be used, for example, for traffic access control. However, an implementation requires an accurate estimation of the MFD with the available data sources. Most scientific literature has considered the estimation of MFDs based on either loop detector data (LDD) or floating car data (FCD). In this paper, however, we propose a methodology for estimating the MFD based on both data sources simultaneously. To that end, we have defined a fusion algorithm that separates the urban network into two sub-networks, one with loop detectors and one without. The LDD and the FCD are then fused taking into account the accuracy and network coverage of each data type. Simulations of an abstract grid network and the network of the city of Zurich show that the fusion algorithm always reduces the estimation error significantly with respect to an estimation where only one data source is used. This holds true, even when we account for the fact that the probe penetration rate of FCD needs to be estimated with loop detectors, hence it might also include some errors depending on the number of loop detectors, especially when probe vehicles are not homogeneously distributed within the network. [ABSTRACT FROM AUTHOR]

This paper proposes an input–output feedback linearization techniques for linear induction motors, taking into consideration the dynamic end-effects. As a main original content, this work proposes a new control law based on the on-line estimation of the induced-part time constant. The estimation law is obtained thanks to a Lyapunov based analysis and thus the stability of the entire control system, including the estimation algorithm, is intrinsically guaranteed. Moreover, with such an approach even the on-line variation of the induced-part time constant with the speed is retrieved, thus improving the behavior of previously developed approaches where such a variation vs. speed is considered a priori known. The proposed control technique, integrating the on-line induced-part time constant estimation, is tested by means of simulations and experiments carried out on a suitably developed test set-up. [ABSTRACT FROM AUTHOR]

Proportionate-type normalized subband adaptive filter (PNSAF-type) algorithms are very attractive choices for echo cancellation. To further obtain both fast convergence rate and low steady-state error, in this paper, a variable step size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is proposed by minimizing the square of the noise-free a posterior subband error signals. A noniterative shrinkage method is used to recover the noise-free a priori subband error signals from the noisy subband error signals. Significantly, the proposed VSS strategy can be applied to any other PNSAF-type algorithm, since it is independent of the proportionate principles. Simulation results in the context of acoustic echo cancellation have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The pitch measurement and AFM calibration precision are significantly influenced by the grating pitch evaluation method. This paper presents the ensemble empirical mode decomposition (EEMD) based pitch evaluation method to relieve the accuracy deterioration caused by high and low frequency components of scanning profile during pitch evaluation. The simulation analysis shows that the application of EEMD can improve the pitch accuracy of the FFT-FT algorithm. The pitch error is small when the iteration number of the FFT-FT algorithms was 8. The AFM measurement of the 500 nm-pitch one-dimensional grating shows that the EEMD based pitch evaluation method could improve the pitch precision, especially the grating line position precision, and greatly expand the applicability of the gravity center algorithm when particles and impression marks were distributed on the sample surface. The measurement indicates that the nonlinearity was stable, and the nonlinearity of x axis and forward scanning was much smaller than their counterpart. Finally, a detailed pitch measurement uncertainty evaluation model suitable for commercial AFMs was demonstrated and a pitch uncertainty in the sub-nanometer range was achieved. The pitch uncertainty was reduced about 10% by EEMD. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to identify population-wide traveler type distribution and simultaneously infer individual travelers’ Origin-Destination (OD) pairs, based on the individual records of a shared mobility (bike) system use in a multimodal travel environment. Given the information about the travelers’ outbound and inbound bike stations under varied price settings, the developed Selective Set Expectation Maximization (SSEM) algorithm infers an underlying distribution of travelers over the given traveler “types,” or “classes,” treating each traveler’s OD pair as a latent variable; the inferred most likely traveler type for each traveler then informs their most likely OD pair. The experimental results based on simulated data demonstrate high SSEM learning accuracy both on the aggregate and dissagregate levels. [ABSTRACT FROM AUTHOR]

Ns-3 simulator provides a great platform for experimental studies on different communication technologies and network topologies, and it is already used by many researchers all around the world. Unfortunately, its current TCP implementation is outdated and not ready for being used as reference platform for TCP-centered research, relegating ns-3 to play a background role in favor of its predecessor, ns-2. This paper presents the update process of ns-3 TCP module, that offers to TCP developers and researchers a consistent and RFC-compliant platform to base their works on. The method involves the addition of a new congestion-based state machine, the split of congestion control out from the socket core, and the development of a very detailed and complete testing framework, which can be easily extended. Performance tests follow the description, comparing RAM usage and execution time between ns-3 releases. Also, a case study over high-delay links on the interaction between the Transmission Control Protocol (TCP) and CoDel, an Active Queue Management algorithm born to control queuing delay, is presented. [ABSTRACT FROM AUTHOR]

A loose part in a nuclear reactor will generate an impact signal. Therefore, determining whether an impact signal exists in the detected signal is an effective method to ascertain whether loose parts are present in a nuclear reactor. In a practical environment, the impact signal may be submerged in background noise. A signal detection method based on the eigenvector algorithm (EVA) is proposed in this paper. Mathematical and statistical characteristic of kurtosis is particularly sensitive to the impact signal. Thus by the characteristics in EVA, extraction of the impact signal under strong noise can be realized. Simulation experiments are carried out to verify the proposed method. The results show that EVA is able to detect the impact signal. [ABSTRACT FROM AUTHOR]

Human guide robots usually generate desired trajectories from human demonstrations. The training process can be in task space or joint space. The task space method needs the inverse kinematics; the joint space method uses dynamic time warping. Both of them destroy the accuracy of the generated trajectory. In this paper, we first use Lloyd's algorithm to encode the input signals such that the observations are time-independent. The desired trajectory is generated in joint space without dynamic time warping. Then we modify the hidden Markov model (HMM) such that it can work in joint space. Since the desired trajectories are the joint angles, they can be applied directly to robot control without calculating the inverse kinematics. Simulation and experimental results show that the modified HMM with Lloyd's algorithm work well in joint space. [ABSTRACT FROM AUTHOR]

The paper comments on the new possibilities of utilizing the inertial navigation system in robototechnics. It deals with the application of a new inertial measurement system for a robotic workplace calibration. The calibration is necessary so that the simulation model of the production device can adjust to the real geometric conditions. The aim is to investigate and develop a new combined inertial navigation system based on electronic gyroscopes, magnetic and barometric sensors. The crucial activity is focused on three basic fields: 1. the first goal is to analyze accelerometer and gyroscopic sensors and their possibilities of utilization for inertial navigation. The simulation of the effect of sensors with different metrological parameters and their effect on the properties of the proposed combined navigation system, 2. the second goal is to optimize a specialized processor system for processing the data from the defined sensors in connection with controlling items of an industrial robot. The proposal of an algorithm of combined navigation with respect to the used processor system and 3. the third goal is to verify experimentally the proposed inertial navigation system in real conditions of the industrial robot operation. [ABSTRACT FROM AUTHOR]

Electron beam welding (EBW) became widely spread in the branches of mechanical engineering connected with manufacturing of aviation and aerospace equipment. At present time much attention is paid to increasing the quality of essential and highly-loaded components obtained by means of EBW. This requirement is closely connected with butt positioning accuracy of the manipulator output members included into an automatic electron beam production complex (AEBPC). The control algorithm of a manipulator output member positioning process is given in the paper for path following based on the developed mathematical model of accounting the kinematic and dynamical characteristics of manipulators included into an automatic electron beam production complex (AEBPC). The feature of the model is accounting the electron beam (EB) deviation from the ideal position based on manipulators errors. Inclusion of the simulation results into the control model of electron beam welding process will allow increasing the quality of weld joints. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiple access passive optical network (OFDMA-PON) has being considered as a promising solution for next generation PONs due to its high spectral efficiency and flexible bandwidth allocation scheme. In order to take full advantage of these merits of OFDMA-PON, a high-efficiency medium access control (MAC) dynamic bandwidth allocation (DBA) scheme is needed. In this paper, we propose two DBA algorithms which can act on two different stages of a resource allocation process. To achieve higher bandwidth utilization and ensure the equity of ONUs, we propose a DBA algorithm based on frame structure for the stage of physical layer mapping. Targeting the global quality of service (QoS) of OFDMA-PON, we propose a full-range DBA algorithm with service level agreement (SLA) and class of service (CoS) for the stage of bandwidth allocation arbitration. The performance of the proposed MAC DBA scheme containing these two algorithms is evaluated using numerical simulations. Simulations of a 15 Gbps network with 1024 sub-carriers and 32 ONUs demonstrate the maximum network throughput of 14.87 Gbps and the maximum packet delay of 1.45 ms for the highest priority CoS under high load condition. [ABSTRACT FROM AUTHOR]

This paper proposes a bearing-only measurement based guidance algorithm for a mobile agent to navigate in a two-dimensional plane. Based on the bearing vectors and the subtended angles related to three stationary beacons, the proposed algorithm allows the agent to reach its desired location. We show that the agent reaches its desired location globally asymptotically based on Lyapunov stability theory. Under some assumptions, the agent is also proved to reach the desired location exponentially based on Lyapunov's indirect method. Simulations and actual experimental tests on quadrotor system are also provided to verify the effectiveness of the proposed algorithm in outdoor environment. [ABSTRACT FROM AUTHOR]

Most studies on the interaction between pantograph and overhead contact systems refer to the conventional catenary composed of contact and messenger wires and droppers, whereas the literature related to the interaction with rigid overhead conductor rails is limited. This paper presents an algorithm capable of simulating the interaction between a catenary represented as a Finite Element Model and a pantograph modeled as a multibody linkage. The algorithm will be proven to be accurate and efficient. The procedure has been used to simulate the interaction in different scenarios as a tool to optimize the design of the infrastructure as well as the configuration of the pantograph. The piece of software produced can consider interaction of one or two pantographs with one or two overhead lines in transition sections. [ABSTRACT FROM AUTHOR]

Controllability of complex networks has been one of the attractive research areas for both network and control community, and has yielded many promising and significant results in minimum inputs and minimum driver vertices. However, few studies have been devoted to studying the minimum controlled vertex set through which control over the network with arbitrary structure can be achieved. In this paper, we prove that the minimum driver vertices driven by different inputs are not sufficient to ensure the full control of the network when the associated graph contains the inaccessible strongly connected component which has perfect matching and propose an algorithm to identify a minimum controlled vertex set for network with arbitrary structure using convenient graph and mathematical tools. And the simulation results show that the controllability of network is correlated to the number of inaccessible strongly connected components which have perfect matching and these results promote us to better understand the relationship between the network’s structural characteristics and its control. [ABSTRACT FROM AUTHOR]

Inspired by the dynamics phenomenon occurred in social networks, the WJJLGS model is modified to imitate the clustering dynamics of signed social networks. Analyses show that the clustering dynamics of the model can be applied to partition signed social networks. Traditionally, blockmodel is applied to partition signed networks. In this paper, a detailed dynamics-based algorithm for signed social networks (DBAS) is presented. Simulations on several typical real-world and illustrative networks that have been analyzed by the blockmodel verify the correctness of the proposed algorithm. The efficiency of the algorithm is verified on large scale synthetic networks. [ABSTRACT FROM AUTHOR]

The multi-objective energy consumption scheduling problem based on the third-party management is one essential issue of smart grid. The minimal energy cost and the maximal utility are two optimization objectives. One characteristic of the multi-objective energy consumption scheduling problem is that the magnitude difference between the two objectives increases as the number of users increases. The difference affects the quality of the solution set in the sense that it is harder to obtain the uniformly distributed solutions with good convergence and diversity for problems with a larger difference. In this paper, we propose a decomposition algorithm based on the non-uniform weight vector. The weight vector is designed based on the relationship of the magnitude difference between the two optimization objectives. The weight vector consists of two parts. One is the balance factor that can maintain the diversity of the solutions. The other is the scale factor that further enhances the searching ability with a large number of users. With the non-uniform weight vector, our algorithm can effectively deal with the magnitude difference between the two optimization objectives. The simulation illustrates that the proposed algorithm is very useful for solving large-scale energy consumption scheduling problems. In addition, the modified standard ZDT problems with apparent magnitude differences between two objectives are used to illustrate the versatility and stability of the proposed algorithms based on the non-uniform weight vector. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of robust track-to-track association in the presence of sensor biases and missed detections. Under the condition of large range biases with sensors, it is validated that the structural difference between two sets of local tracks from different sensors can be described by a non-rigid transformation. After that, we turn the robust track-to-track association problem into the non-rigid point matching problem in the framework of TPS-RPM (Thin Plate Spline-Robust Point Matching). Further, to improve the performance of the track-to-track association, the structural feature is introduced for each local track, and the structural similarity is incorporated by regularizing the energy function of the TPS-RPM algorithm. Simulation results demonstrate the effectiveness of the proposed approaches compared with competing algorithms. [ABSTRACT FROM AUTHOR]

The paper proposes an aggregate battery modelling approach for an (electric vehicle) EV fleet, which is aimed for energy planning studies of EV-grid integration. The proposed model improves on the existing, basic aggregate battery modelling approach by accounting for a variable structure of the aggregate battery systems, variable (state of charge) SoC constraints and specific input time-distributions such as those of average SoC at destination and number of arriving and departing vehicles. In the particular case-study presented, the input distributions are reconstructed from a large set of delivery vehicle fleet driving missions, including simulation of individual vehicle behaviours over the full set of driving cycles. The charging power input is obtained by using a dynamic programming-based optimisation algorithm aimed at finding a global optimum in terms of minimised electricity cost. For the purpose of proposed model validation and its comparison with the basic model, a distributed fleet vehicle model is developed, where a specific algorithm is proposed for distributing the optimised charging power input to charging inputs of individual vehicles. [ABSTRACT FROM AUTHOR]

The main result of this paper is a simulation algorithm which, under quite general constraints, transforms algorithms running in the Congested Clique model into algorithms running in the MapReduce model. As a case study of applying this simulation algorithm, we first present a distributed O ( Δ ) -coloring algorithm running on the Congested Clique in O ( 1 ) rounds, if Δ ≥ log 2 ⁡ n , and O ( log ⁡ log ⁡ log ⁡ n ) rounds otherwise. Applying the simulation theorem to this Congested Clique O ( Δ ) -coloring algorithm yields an O ( 1 ) -round O ( Δ ) -coloring algorithm in the MapReduce model. We apply our simulation algorithm to other Congested Clique algorithms including the 2-ruling set and metric facility location algorithms of Berns et al. (ICALP 2012). Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the CONGEST model), the major impediment to constructing fast algorithms is the O ( log ⁡ n ) restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for fault detection and classification using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation on parallel transmission. Alpha and beta (mode) currents generated by Clarke’s transformation were used to convert the signal of discrete wavelet transform (DWT) to get the wavelet transform coefficients (WTC) and the wavelet energy coefficient (WEC). Daubechies4 (Db4) was used as a mother wavelet to decompose the high frequency components of the signal error. The simulation was performed using PSCAD/EMTDC for transmission system modeling. Simulation was performed at different locations along the transmission line with different types of fault and fault resistance, fault location and fault initial angle on a given power system model. Four statistic methods utilized are in the present study to determine the accuracy of detection and classification faults. The results show that the best Clarke transformation occurred on the configuration of 12-24-48-4, respectively. For instance, the errors using mean square error method, the errors of BPNN, Pattern Recognition Network and Fit Network are 0.03721, 0.13115 and 0.03728, respectively. This indicates that the BPNN results are the lowest error. [ABSTRACT FROM AUTHOR]

Feature selection is an important preprocessing step in data mining. Mutual information-based feature selection is a kind of popular and effective approaches. In general, most existing mutual information-based techniques are greedy methods, which are proven to be efficient but suboptimal. In this paper, mutual information-based feature selection is transformed into a global optimization problem, which provides a new idea for solving feature selection problems. First, a single-objective feature selection algorithm combining relevance and redundancy is presented, which has well global searching ability and high computational efficiency. Furthermore, to improve the performance of feature selection, we propose a multi-objective feature selection algorithm. The method can meet different requirements and achieve a tradeoff among multiple conflicting objectives. On this basis, a hybrid feature selection framework is adopted for obtaining a final solution. We compare the performance of our algorithm with related methods on both synthetic and real datasets. Simulation results show the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

In drilling technology, adequate control over the distance between adjacent sections of twin parallel horizontal wells has a significant impact on the final recovery rate from unconventional oil and gas resources, such as heavy oil and natural gas hydrates. For this distance to meet design requirements and to reduce the duration of the drilling period, it is necessary to use a rotating magnet ranging system that helps drill an injection well starting from the build-up section. Usually, the build-up sections of the twin parallel horizontal wells are not parallel. Therefore, the spatial relationship between the build-up sections of the two wells can be divided into two types, namely, converging coplanar and non-coplanar. In this paper, we propose a method based on an algorithm for finding extreme values to calculate the radial distance between the build-up sections of the twin parallel horizontal wells. In addition, we propose and discuss a method for calculation of the direction between the magnetic sub and the probe tube in the build-up sections using coordinate transformations combined with the rotating magnet ranging calculation for the horizontal sections of twin parallel horizontal wells. The simulation results indicate that the magnet ranging algorithm for the build-up sections of twin parallel horizontal wells has a precision of a few percent and can meet the actual needs of the drilling site. [ABSTRACT FROM AUTHOR]

As an important data source in 3D GIS, 3D landmark models are built to simulate the real-world scenario. However, due to the enormous volume and complexity of 3D models, the data transmission under limited bandwidth and the real-time rendering have always been an open problem. In order to improve the visualization and the efficiency, this paper proposes a novel model simplification algorithm in consideration of texture after the analysis of the existing model simplification approaches. Differing from the previous research, our approach defines a new error metric related to the model texture, which extends the vertex clustering scheme in 3D geometry space and 2D texture space independently. Since the uneven distribution of vertices is taken into account, the clustering unit is divided adaptively in consideration of both geometry and texture information. In view of reducing the memory overhead and improving the algorithm efficiency, we don’t create new vertices by iterative calculations, but use the inherent vertices in the initial meshes as the characteristic vertices. To demonstrate the feasibility and effectiveness of our strategy, a series of simplification experiments have been carried out on the platform of DirectX 3D, a widely used 3D application programming interface. The results show that the simplified models in consideration of texture preserve more texture details than those traditional ones. It apparently makes a good balance between the reduction rate and visual effects. [ABSTRACT FROM AUTHOR]

Wire-driven flexible robots are efficient devices for minimally invasive surgery, since they can work well in complex and confined environments. However, the real-time position and shape information of the robot cannot be well estimated, especially when there is a payload on the end effector. In this paper, a novel tracking and shape reconstruction method for a wire-driven flexible robot is proposed. The proposed method utilizes the length of the robot as well as the positional and directional information measured by an electromagnetic sensor mounted at the distal end of the robot. A reconstruction algorithm, which is based on a three order Bézier curve, is then carried out utilizing the positional and directional information along with the length information of the robot. This method can provide good tracking results and high shape reconstruction accuracy with limited modification of the robot. Compared with other reconstruction methods, the proposed method does not rely on any mechanical model of the robot. Therefore, the method works well with an unknown payload. The feasibility of the proposed method is verified by both simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive least squares support vector machine (LSSVM) model with a novel update to tackle process variations. The key idea of the update is to divide the process variations into two main categories, namely, irreversible and reversible variations. Correspondingly, sample addition and sample replacement are proposed to update the model. The incremental LSSVM algorithm and detailed update procedure are also provided. A benchmark simulation with a time-varying nonlinear function is conducted to evaluate the effectiveness of the update algorithm. Finally, the proposed method is applied to predict the nitrogen oxide (NOx) emissions of a coal-fired boiler using real operation data from a power plant. Results reveal that the LSSVM model with the novel update maintains high prediction accuracy despite different process characteristics. Meanwhile, the time consumed in the update process is decreased because of the incremental form compared with the model reconstruction. [ABSTRACT FROM AUTHOR]

Backlight dimming can help LCDs reduce power consumption and improve CR. With fixed parameters, dimming algorithm cannot achieve satisfied effects for all kinds of images. The paper introduces an image-classification-based global dimming algorithm. The proposed classification method especially for backlight dimming is based on luminance and CR of input images. The parameters for backlight dimming level and pixel compensation are adaptive with image classifications. The simulation results show that the classification based dimming algorithm presents 86.13% power reduction improvement compared with dimming without classification, with almost same display quality. The prototype is developed. There are no perceived distortions when playing videos. The practical average power reduction of the prototype TV is 18.72%, compared with common TV without dimming. [ABSTRACT FROM AUTHOR]

High-performance computing is important in many areas of engineering. Increasing capabilities of modern workstations open new possibilities in scientific computing. This paper demonstrates how graphical processing units can be used efficiently for large models of elastic wave propagation in complex media. The method is based on the local interaction simulation approach and a parallel algorithm architecture. The focus of the work presented is on numerical implementation and covers aspects related to software modular architecture, computer memory organisation and optimisation. A domain decomposition approach allowing for calculations using multiple-GPU configurations is proposed, implemented and examined. The performance of the proposed simulation framework is tested for numerical models of different sizes, various computing precisions and hardware platforms. The results obtained are discussed in terms of graphical processing unit limitations. Obtained results indicate significant speed-up factors comparing to calculations using central processing units or different modelling approaches. [ABSTRACT FROM AUTHOR]

Fault detection and isolation for two-dimensional (2-D) systems represent a great challenge in both theoretical development and applications. Use of Kalman filters in fault detection has been well developed for one-dimensional (1-D) system. In this paper, a fault detection algorithm is developed for 2-D systems described by the Fornasini & Marchesini (F–M) model. Based on the state estimate from a recursive 2-D Kalman filter, a residual is generated. From the model of residual over a 2-D evaluation window, the residual is explicitly related to faults within the evaluation window. Using generalized likelihood ratio (GLR), residual evaluation is carried out and compared with a calculated threshold value for fault detection. Simulation results indicate that faults can be detected effectively using the proposed method. [ABSTRACT FROM AUTHOR]

In this paper second-order seismic wave equations are adopted to simulate the wave propagation in the presence of a free-surface. In the simulations, horizontal and vertical spatial derivatives are solved by Fourier and Chebyshev pseudo-spectral algorithms respectively. In addition, Fourier–Chebyshev pseudo-spectral method is also used to solve the boundary conditions of the free-surface, which are Neumann boundaries. That makes results at the free-surface keep same high precision with inner domain. The new scheme is tested against Lamb's problem in a uniform elastic half space. Agreement between numerical and analytical results is every good. Compared with the former simulation scheme based first-order velocity–stress equations, the new scheme based on second-order equations is more efficient because it has fewer controlling equations. [ABSTRACT FROM AUTHOR]

The approach of applying a cascaded network consisting of radial basis function nodes and least square error minimization block to Compressed Sensing for recovery of sparse signals is analyzed in this paper to improve the computation time and convergence of an existing ANN based recovery algorithm. The proposed radial basis function—least square error projection cascade network for sparse signal Recovery (RASR) utilizes the smoothed L 0 norm optimization, L 2 least square error projection and feedback network model to improve the signal recovery performance over the existing CSIANN algorithm. The use of ANN architecture in the recovery algorithm gives a marginal reduction in computational time compared to an existing L 0 relaxation based algorithm SL0. The simulation results and experimental evaluation of the algorithm performance are presented here. [ABSTRACT FROM AUTHOR]