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The stability of cable-net structures depends on the prestress of the system. Due to the large displacement and mutual effect of the cables, it is difficult to simulate the tensioning process and control the forming accuracy. The Backward Algorithm (BA) has been used to simulate the tensioning process. The traditional BA involves complicated and tedious matrix operations. In this paper, a new numerical method based on the Vector Form Intrinsic Finite Element (VFIFE) method is proposed for BA application. Moreover, the tensioning sequence of a complex cable-net structure is introduced. Subsequently, a new approach for BA application in the simulation of the tensioning process is presented, which combines the VFIFE approach and the notion of form-finding. Finally, a numerical example is simulated in detail and the results of different tensioning stages are analyzed to verify the feasibility of the proposed approach. This study provides a significant reference for improving the construction control and forming accuracy of complex prestressed cable-net structures. [ABSTRACT FROM AUTHOR]

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Comments on the Cathy McGeoch's paper 'Toward an Experimental Method for Algorithm Simulation.' Types of research within the computational testing literature; Information on algorithmic simulation as a tool to study the properties of algorithms; Treatment algorithms by McGeoch; Relation of central processing unit time to the basic aspects of an algorithm.

Multiple supply voltage based ( V) Systems on Chip (SoCs) allow designers to implement large, complex systems for diverse applications. However, the need for level conversion imposes penalties and often results in non-optimal SoCs. Thus, the level converters are overhead for the circuits in which they are being used. If power consumption of the level converters continues to grow, then they will fail to serve the very purpose for which they were built. This paper proposes the power (leakage)-delay optimization of a DC to DC universal voltage level converter (ULC) using a dual- T (dual-oxide CMOS or DOXCMOS) technique and exploiting transistor geometry. The proposed ULC is a novel circuit proposed here for the first time and performs level-up, level-down conversion, or blocking of the input signal, based on the requirements. The paper further proposes a novel design methodology accompanied by an optimization algorithm for the parasitic-aware power-delay optimization of the ULC circuit. The entire design has been implemented in 90 nm CMOS up to layout, including DRC/LVS and parasitic (RC) re-simulation, and was subjected to process variation of 10 process parameters. The optimal ULC with 20 transistors yields power savings of 87.5 %, delay improvement of 87.3 % and area savings of 21 % over the baseline design. It is a robust design performing a stable voltage level conversion for voltages as low as 0.6 V (50 % of V) and loads varying from 10 to 200 fF. [ABSTRACT FROM AUTHOR]

In substations with an inner bridge connection, the two main transformers are generally connected in parallel. The differential protection of the transformer, under normal operating conditions, may maloperate when the other transformer is switching on through the bridge breaker after scheduled maintenance. This paper analyzes the factors that may lead to the maloperation of transformer differential protection and concludes that local transient saturation of the current transformer (CT) caused by the decaying dc component in the magnetizing inrush is the main cause of the maloperation of the transformer differential protection. By detecting the time difference between the instant of sudden changes occurring in the transformer wye-side current and the instant of the differential current increasing, a new algorithm based on the time differential method is proposed in this paper, which can prevent the maloperation of transformer differential protection in substations with an inner bridge connection. On the basis of detailed analysis and simulation work, the new algorithm is verified to be applicable under the conditions of sympathetic inrush and CT saturation. [ABSTRACT FROM AUTHOR]

This paper studies the application of extreme value statistics (EVS) theory on analysis for stock data, based on interior penalty function algorithm and Bootstrap methods. The generalized Pareto distribution (GPD) models are considered in analyzing the closing price data of Shanghai stock market. The maximum likelihood estimates (MLEs) are obtained by using the interior penalty function algorithm. Correspondingly, the bias and standard errors of MLEs, and the hypothesis test on the shape parameter are concerned through Bootstrap methods. Some simulations are performed to demonstrate the efficacy of parameter estimation and the power of the test. The estimates of the tail index in this paper are compared with those obtained via classical methods. At last, the model is diagnosed by numerical and graphical methods and the Value-at-Risk (VaR) is estimated. [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]

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]

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]

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]

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]

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]

In practice, most projects result in cost overruns and schedule slippage due to poor resource management. This paper presents an approach that aims at reducing project duration and costs by empowering project managers to assess different scenarios. The proposed approach addresses combinatorial modes for tasks, multi-skilled resources, and multiple calendars for resources. A case study reported in the literature is presented to demonstrate the capabilities of this method. As for practical implications, this approach enhances the decision-making process which results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this paper adds empirical evidence to enrich the existing literature, as it highlights relevant issues to model properly the complexity of real-life projects. [ABSTRACT FROM AUTHOR]

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient. [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: 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]

In order to understand the role of crossover in differential evolution, theoretical analysis and comparative study of crossover in differential evolution are presented in this paper. Two new crossover methods, namely consecutive binomial crossover and non-consecutive exponential crossover, are designed. The probability distribution and expectation of crossover length for binomial and exponential crossover used in this paper are derived. Various differential evolution algorithms with different crossover methods including mutation-only differential evolution are comprehensively compared at system level instead of parameter level. Based on the theoretical analysis and simulation results, the effect of crossover on the reliability and efficiency of differential evolution algorithms is discussed. Some insights are revealed. [ABSTRACT FROM AUTHOR]

This paper introduces a new noniterative fault-location algorithm for long transmission lines compensated by a series flexible ac transmission systems (FACTS) device. In the proposed algorithm, synchronous voltage and current samples from both ends of the transmission line are used and the distributed parameter line model in the time domain is applied. Due to the difficulties in the modeling of the series FACTS devices during a fault, the presented method does not use the model of the series devices. In this paper, the fault-location problem is converted to an optimization one in which the location and resistance of the fault are the decision variables. The proposed method includes three stages. Two fault locations are obtained, corresponding to the first two stages. In stage three, the obtained results from stages 1 and 2 are compared, and the correct fault location is selected. The proposed algorithm is a noniterative one and it is suitable for transmission lines compensated by any series FACTS device which could have the overvoltage protection and can be in any operating mode. The performance of the proposed algorithm is evaluated under different structural and fault conditions. The simulation results confirm the accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wireless monitoring and control technique is vital to improve the performance of wireless networks, e.g., helping improve the efficiency of scheduling. In this paper, we focus on two centralized algorithms based on IEEE 802.16 standard to adopt effective monitor and control mechanism to do the scheduling and channel assignment in WiMAX mesh networks. The base station (BS) monitors the subscriber stations (SSs) and gathers their requests, and then controls the SSs under the direction of the centralized algorithm. The IEEE 802.16 standard proposes two kinds of scheduling algorithms in WiMAX mesh networks, centralized and distributed algorithms. The centralized solution is more convenient to monitor and control a WiMAX mesh network since the central node is the unique monitor and controller. This paper mainly introduces two schemes working in a centralized mode. They are working in a tree search active link selection (TSALS) fashion and a link search active link selection (LSALS) fashion. In these schemes, each node is equipped with a third generation (3G) mesh router, which owns two relay transceivers and can be tuned among multiple channels. The major goal of our solution is to reduce the length of scheduling under an interference-aware restriction. The feasibility and efficiency of our proposal are testified through extensive simulations. The result justifies that our algorithms provide much higher performance than the previous ones. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the present paper, preliminary results towards the generalization to the infinite-dimensional setting of some well-known robust finite-dimensional control algorithms are illustrated. More specifically, we deal with the tracking problem for some classes of linear uncertain infinite-dimensional systems evolving in Hilbert spaces. We design distributed variable-structure stabilizers that are shown to be effective in the presence of external disturbances. The main focus of the present paper is on the rejection of nonvanishing external disturbances. The generalization to the infinite-dimensional setting of the well-known finite-dimensional controllers, namely the "power-fractional" controller [S. P. Bhat and D. S. Bernstein, SIAM J. Control Optim., 38 (2000), pp. 751-766] and two second-order sliding-mode" control algorithms (the "twisting" and "supertwisting" algorithms [L. Fridman and A. Levant, Higher order sliding modes as a natural phenomenon in control theory, in Robust Control via Variable Structure and Lyapunov Techniques, Springer-Verlag, Berlin, 1996, pp. 107-133; A. Levant, Internat. J. Control, 58 (1993). pp. 1247-1263]), is the main contribution of the present investigation. First, the "distributed twisting" control algorithm is developed In address the asymptotic state tracking of the perturbed wave equation. Next, the finite-time state tracking of the unperturbed heat equation is provided by means of a "distributed power-fractional" controller. Finally, the "distributed supertwisting" controller is suggested to address the asymptotic state tracking of the heat equation in spite of the presence of persistent disturbances. Constructive proofs of stability are developed via the Lyapunov functional technique, which leads to simple tuning rules for the controller parameters. Simulation results are discussed to verify the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper reviews the Bayesian melding approach while a companion technical report cited in the paper gives technical details about the Gibbs sampling algorithm used to implement the model and software developed for the research reported in this paper and available online can enable the method's use in other applications. This paper critically assesses the use of melding for mapping ozone concentration fields for possible future use in setting regulatory standards. This assessment has two stages. First a simulation study validates the computer code and goes on to investigate properties of the melding approach in situation where the 'truth' is known. Then it is critically tested on a ozone mapping application using ozone data from the air quality system (AQS) database and simulated data from the multiscale air quality simulation platform (MAQSIP) chemical transportation model for ozone. In all cases, the melding is testing against Kriging, a simpler and more traditional way of mapping spatial fields. Conclusions and recommendations for future work are provided. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people's health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) enables high speed vehicles to communicate with each other. This kind of communication can provide road safety and passengers' comfort. Covert channels are used to transmit information secretly over the network. Network covert channel is not only used as a hacking tool, but also used to convey secret information such as private keys. Unlike wired and conventional wireless networks, few studies are conducted on covert communication in VANET. The goal of this paper is to develop a hybrid (timing and storage) covert channel in VANET. In the timing part, covert messages are sent by altering the timing pattern of the service and control packets. The proposed covert timing algorithm is dynamically changed based on the vehicular traffic volume in the transmitter's radio range. This dynamism is used to achieve better covert capacity with an acceptable error rate. On the other hand, some fields of the periodic status messages, sent in the control channel, are utilized in the storage part. An encoding algorithm is also proposed to embed the covert data in the mentioned covert timing and storage opportunities. The encoding algorithm provides a high embedding capacity, even if the number of opportunities' possible values is not any power of two. Finally, the transmitted secret data volume, the packet loss ratio, the channel error rate and the effect of the proposed method on other vehicles' throughput are evaluated in a simulation process. [ABSTRACT FROM AUTHOR]

Delay tolerant network is a type of network where the end-to-end path is not available from source to destination due to the node mobility, dynamic topology and network partitioning or such a path is highly unstable and may split almost immediately after it has been explored. In this background, existing ad hoc routing protocols would be unsuccessful. Therefore, the concept of store-carry-forward mechanism is utilized by introducing a new layer on the top of transport layer called as bundle layer. The bundle layer store message(s) in a finite size buffer for the long duration of time. As a result, node buffer runs out of space and drop messages to overcome congestion. In this paper, we aim to schedule the node buffer by using local knowledge-based buffer management policies called as best message-size selections buffer management policy. We have utilized the local information available in message header such as message-size to control the drop of messages. The evaluation of proposed policy with the existing DOA, DLA, LIFO, MOFO, N-drop and SHLI have been analyzed in ONE simulator in terms of reducing message drop, overhead, latency and raising the delivery ratio. [ABSTRACT FROM AUTHOR]

Dynamic Travelling Salesman Problem (DTSP) is a novel type of TSP where the number of cities in the problem domain changes unpredictably. The approaches to handling dynamism in those DTSPs, has been solving the problems as they were static and recreating the models after each change. In this respect, multi-agent based strategies along with intelligent approaches provide an opportunity to deal with those difficulties. The proposed approach in this paper is based on the modification of existing solutions according to changes in the city domain. Thereby problem is not resolved while local city agents deliver their novel bids (solution proposals) for these new conditions. Finally, general manager agent makes a decision about the new solution. This study presents two different agent-based solution strategies for providing promising solutions to DTSP. One of these strategies is based on the competition of city agents in a greedy way and thereby city agents just search for randomly selected alternatives which are feasible for the new conditions. The second strategy covers competition of city agents by the use of great deluge algorithm as the search mechanism. Finally, both of those proposed strategies are compared against the solutions of reinvented models. Agent-based strategies start to produce better results as the problem size increases. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.Design/methodology/approach In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems.Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.Originality value An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical investigation of the general problem of emergent irreducible information in networked populations of computable systems. In particular, we narrow our scope to study this problem in algorithmic networks composed of randomly generated Turing machines that follow a susceptible-infected-susceptible contagion model of imitation of the fittest neighbor. We show that there is a lower bound for the stationary prevalence (i.e., the average density of infected nodes by the fittest nodes) that triggers expected (local) emergent openendedness, that is, that triggers an unlimited increase of the expected local emergent algorithmic complexity (or information) of a node as the population size grows. In addition, we show that static networks with a power-law degree distribution following the Barabási-Albert model satisfy this lower bound and thus display expected (local) emergent open-endedness. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) have been more and more widely used recently, in this paper, an electric vehicle handling routing and battery swap station location problem (EV-HR-BSSL) is presented to extend their range of application, which aims to determine the handling routing plan of a fleet of EVs and the location decisions of battery swap stations (BSSs) simultaneously under limited battery driving range. A mathematical problem is developed to illustrate the identified problem, where a fleet of identical EVs are assumed to fulfil the demand of stations for automotive assembly lines based on in-plant milk run handling strategy. Subsequently, several definitions and properties are proposed to solve this model more efficiently. A two-phase dynamic programming method is adopted to obtain the global optimum for small scale problems. For medium and large scale problems, an improved discrete cuckoo search algorithm is developed. The performances are evaluated in simulation and the results indicate that the proposed algorithm is valid and feasible. [ABSTRACT FROM AUTHOR]

In this paper, we consider an exact test of goodness of fit for binomial distribution in sparse data situation. A conventional way is viewing this problem as an independence test problem of a two-way contingency table. We propose an approach to promote the efficiency of the Diaconis-Sturmfels (DS) algorithm when n (sample size) is much larger than m [the first parameter of a binomial distribution B(m, p)] through representing the data and then utilizing minimal Markov bases of the corresponding multinomial model. Simulation results and real data analysis indicate that our method makes the DS algorithm computationally faster. [ABSTRACT FROM AUTHOR]

Radio propagation prediction simulation methods based on deterministic technique such as ray launching is extensively used to accomplish radio channel characterization. However, the superiority of the simulation depends on the number of rays launched and received. This paper presented the indoor three-dimensional (3D) Minimum Ray Launching Maximum Accuracy (MRLMA) technique, which is applicable for an efficient indoor radio wave propagation prediction. Utilizing the novel MRLMA technique in the simulation environment for ray lunching and tracing can drastically reduce the number of rays that need to be traced, and improve the efficiency of ray tracing. Implementation and justification of MRLMA presented in the paper. An indoor office 3D layouts are selected and simulations have been performed using the MRLMA and other reference techniques. Results showed that the indoor 3D MRLMA model is appropriate for wireless communications network systems design and optimization process with respect to efficiency, coverage, number of rays launching, number of rays received by the mobile station, and simulation time. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic model-based design of a control system and an approach toward a drive selection of a centrifuge motion simulator (CMS). The objective of the presented method is to achieve the desired performance while taking into account the complexity of the control system and the overall device cost. An estimation of a dynamic interaction of the interconnected CMS links motions is performed using the suitable inverse dynamics simulation. An algorithm based on the approximate inverse dynamics model is used within the drive selection method. The model of the actuator's mechanical subsystem includes the effective inertia (inertia reflected on the rotor shaft) calculated from the inverse dynamics model. A centralized control strategy based on a computed torque method is considered and compared to traditional decentralized motion controllers. To obtain an accurate comparison of the suggested control methods through a realistic simulation, structural natural frequencies of the manipulator links are considered, and the actuator capabilities are taken into account. The control system design and simulation methods and the drive selection strategies, presented here for the CMS, are applicable within the general robot manipulator's domain. [ABSTRACT FROM AUTHOR]

This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The spatial modulation coincidence imaging (SMCI), as a novel kind of microwave coincidence imaging method, is proposed in this paper. The SMCI system provides a new way to produce the time-space independent signal instead of multitransmitter architecture with wideband randomly modulated signal in radar coincidence imaging. Due to some special features, metamaterial plate is utilized as the reflective surface to modulate the incident signal to construct random radiation field. The resolution of SMCI system is analyzed under large viewing angle with two different transmitting signals. Reflective surface is nonuniformly divided to derive the expression of resolution. The analysis results show that the resolution of SMCI system is mainly determined by the size of reflective surface and center frequency, which is similar to the traditional aperture. The SMCI system is low cost and flexible in design. Simultaneously, it can avoid the synchronization problem between subsources. Moreover, the SMCI system can achieve the resolution of space target through single transmitter–single receiver radar system. High-resolution image can be reconstructed since the tests are nonlinear. Finally, a series of simulation experiments is presented based on the nondirect-viewing scene we proposed. Using the algorithm based on a compressed sensing theory, we reconstructed the target image with high resolution. [ABSTRACT FROM AUTHOR]

This paper deals with grid integration of photovoltaic systems through single-phase unified power quality conditioner (PV-1UPQC) based on notch filter novel control algorithm for its function such as better phase detection, voltage sag/swell, voltage unbalance, voltage and current harmonics eliminations. A notch filter-based control algorithm includes a phase-locked loop (PLL) mechanism which is responsible to avoid multiple zero crossing at the time of highly distorted grid voltage detection. Notch filter PLL-based control algorithm is applied to both series and shunt inverters of PV-tied UPQC. In addition to normalizing voltage and current perturbations, proposed controller has feature of phase detection and perfect grid synchronization. Synchronous reference d-q frame controller and unit vector control algorithm with conventional PLL is also studied, utilized and evaluated for control of PV-1UPQC. Hardware setup is developed in laboratory using DS1103 dSPACE processor. Performance and efficiency of PV-1UPQC are analyzed through simulation and experimentation for various operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. [ABSTRACT FROM AUTHOR]

This paper provides an integrated algorithm to identify a cutset for a large power system for the application of a slow coherency based controlled islanding scheme. Controlled islanding is employed as a corrective measure of last resort to prevent cascading outages caused by large disturbances. The large scale power system is represented as a graph and a simplification algorithm is used to reduce the complexity of the system. Generators belonging to the same slowly coherent group are collapsed into a dummy node, and a graph partition library is used to split the graph into a given number of parts. Some extra islands formed by the partition library are merged into their adjacent large islands and the original cutset of the actual power system is recovered from the highly simplified graph. A software package was developed to test the efficiency of the algorithm, and dynamic simulations were run on the WECC system to verify the effectiveness of the cutset obtained. The WECC system has more than 15 000 buses and 2300 generators. Detailed steps to develop an islanding strategy for a specified contingency for a large system are described in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to design a decentralized robust controller for static synchronous compensators (STATCOMs) using minimax linear quadratic output-feedback control design approach. There is an increase in the available (dynamic) transfer capability of power systems with fixed-speed wind generators due to the designed decentralized controllers. The effects of the integration of various types of wind generators into power systems based on transfer limit have also been analyzed in this paper. The effectiveness of the suggested control strategy is validated by simulations on a benchmark two area power system. The performance of the designed controller is also compared to a conventional proportional-integral-based STATCOM controller. Simulation results show that both the dynamic voltage stability and the transient stability can be improved by using the robust STATCOM control proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a learning-automata-like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL Random Early Detection (LALRED), is founded on the principles of the operations of existing RED congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes. [ABSTRACT FROM AUTHOR]

This paper introduces Bayes risk (expected loss) as a criterion for linear equalization. Since the probability of error is equal to the Bayes risk (BR) for a particular binary loss function, this work is a natural generalization of previous works on minimum probability of error (PE) equalizers. Adaptive equalization algorithms are developed that minimize the BR. Like the minimum PE equalizers, the BR algorithms have low computational complexity which is comparable to that of the LMS algorithm. The advantage of the BR criterion is that the loss function can be specified in a manner that accelerates adaptive equalizer convergence relative to the minimum PE adaptive algorithm as illustrated in simulation examples. Besides introducing a new criterion, this paper provides another independent contribution to the field of PE minimizing equalization. While most prior works focus on M-ary QAM type modulations with rectangular decision regions, this paper uses upper bounds on the probabilities of certain events to yield tractable mathematics that apply to two-dimensional constellations with arbitrarily shaped decision regions. The resulting adaptive algorithm use the full information available in the phase of the error signal, whereas previous algorithms use a quantized version of this error phase. [ABSTRACT FROM AUTHOR]

This paper presents two symbolic model checking algorithms for the verification of analog/mixed-signal circuits. The first model checker utilizes binary decision diagrams while the second is a bounded model checker that uses a satisfiability modulo theory solver. Both methods have been implemented, and preliminary results are promising. [ABSTRACT FROM AUTHOR]

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. [ABSTRACT FROM AUTHOR]

Previous research on canal automation has dealt with the control of single, in-line canals, while canal operators typically have to control an entire network of canals. Because the branches in a network are hydraulically coupled with each other, control of a branching canal network based on separate controllers for each branch may not be the most effective control strategy. A methodology by which existing automatic control systems could be modified to control branching canal networks is provided in a companion paper. This paper presents results of hydraulic simulations of the new methodology to estimate the controllability of a large portion of the branching canal network operated by the Salt River Project (SRP). Two types of controllers were used for this study: (1) linear quadratic regulator (LQR) and (2) model predictive control (MPC). Both controllers used the same underlying process model [integrator-delay (ID) model], and both controllers were capable of feedback and feedforward control. Under feedback control alone, both controllers gave similar performance, but were unable to effectively control the overall system because of the long delay times. When feedforward control was added to the feedback controller, both of these control systems were able to effectively control the branching canal network operated by SRP. For the LQR controller, the volume compensation method for routing known demand change was used as the feedforward controller. For the MPC controller, the ID model was used as the feedforward controller. Slight differences were noted between the performance of the two feedforward controllers. [ABSTRACT FROM AUTHOR]

Techniques to determine subarrays when processing elements of VLSI arrays become faulty have been investigated extensively. These tend to identify the largest subarray that is possible without concentrating on the power efficiency of the resulting subarray. In this paper, we propose new techniques, based on heuristic strategy and dynamic programming, to minimize the interconnect length in an attempt to reduce power dissipation without performance penalty. Our algorithms show that notable improvements in the reduction of the number of long interconnects could be realized in linear time and without sacrificing the size of the subarray. Our evaluations show that, for a VLSI array of size 256 x 256, the number of long interconnects in the subarray can be reduced by up to 95 percent for clustered faults and up to 50 percent and 73 percent for a random fault with density of 10 percent and 0.1 percent, respectively, when compared with the most efficient implementation cited in the literature. The interconnect power saving for a VLSI array of size 512 · 512 is by up to 11 percent for a random fault. We have also shown that interconnect power savings of up to 14 percent are possible for the cases investigated. Simulations based on several random and clustered fault scenarios clearly reveal the superiority of the proposed techniques for power efficient realizations. In addition, the lower bound of the performance has been proposed to demonstrate that the proposed algorithms are nearly optimal for the cases considered in this paper. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of unsupervised Bayesian hidden Markov chain restoration. When the hidden chain is stationary, the classical "Hidden Markov Chain" (HMC) model is quite efficient, and associated unsupervised Bayesian restoration methods using the "Expectation-Maximization" (EM) algorithm work well. When the hidden chain is non stationary, on the other hand, the unsupervised restoration results using the HMC model can be poor, due to a bad match between the real and estimated models. The novelty of this paper is to offer a more appropriate model for hidden nonstationary Markov chains, via the theory of evidence. Using recent results relating to Triplet Markov Chains (TMCs), we show, via simulations, that the classical restoration results can be improved by the use of the theory of evidence and Dempster-Shafer fusion. The latter improvement is performed in an entirely unsupervised way using an original parameter estimation method. Some application examples to unsupervised image segmentation are also provided. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a meshfree algorithm based on local radial basis functions (RBFs) combined with the differential quadrature (DQ) method to provide numerical approximations of the solutions of time-dependent, nonlinear and spatially one-dimensional reaction-diffusion systems and to capture their evolving patterns. The combination of local RBFs and the DQ method is applied to discretize the system in space; implicit multistep methods are subsequently used to discretize in time. Design/methodology/approach: In a method of lines setting, a meshless method for their discretization in space is proposed. This discretization is based on a DQ approach, and RBFs are used as test functions. A local approach is followed where only selected RBFs feature in the computation of a particular DQ weight. Findings: The proposed method is applied on four reaction-diffusion models: Huxley's equation, a linear reaction-diffusion system, the Gray–Scott model and the two-dimensional Brusselator model. The method captured the various patterns of the models similar to available in literature. The method shows second order of convergence in space variables and works reliably and efficiently for the problems. Originality/value: The originality lies in the following facts: A meshless method is proposed for reaction-diffusion models based on local RBFs; the proposed scheme is able to capture patterns of the models for big time T; the scheme has second order of convergence in both time and space variables and Nuemann boundary conditions are easy to implement in this scheme. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented. [ABSTRACT FROM AUTHOR]