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In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

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

Ellipse fitting is a highly researched and mature topic. Surprisingly, however, no existing method has thus far considered the data point eccentricity in its ellipse fitting procedure. Here, we introduce the concept of eccentricity of a data point, in analogy with the idea of ellipse eccentricity. We then show empirically that, irrespective of ellipse fitting method used, the root mean square error (RMSE) of a fit increases with the eccentricity of the data point set. The main contribution of the paper is based on the hypothesis that if the data point set were pre-processed to strategically add additional data points in regions of high eccentricity, then the quality of a fit could be improved. Conditional validity of this hypothesis is demonstrated mathematically using a model scenario. Based on this confirmation we propose an algorithm that pre-processes the data so that data points with high eccentricity are replicated. The improvement of ellipse fitting is then demonstrated empirically in real-world application of 3D reconstruction of a plant root system for phenotypic analysis. The degree of improvement for different underlying ellipse fitting methods as a function of data noise level is also analysed. We show that almost every method tested, irrespective of whether it minimizes algebraic error or geometric error, shows improvement in the fit following data augmentation using the proposed pre-processing algorithm. [ABSTRACT FROM AUTHOR]

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

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

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

As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks. [ABSTRACT FROM PUBLISHER]

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

In this paper, the conditional distance correlation (CDC) is used as a measure of correlation to develop a conditional feature screening procedure given some significant variables for ultrahigh-dimensional data. The proposed procedure is model free and is called conditional distance correlation-sure independence screening (CDC-SIS for short). That is, we do not specify any model structure between the response and the predictors, which is appealing in some practical problems of ultrahigh-dimensional data analysis. The sure screening property of the CDC-SIS is proved and a simulation study was conducted to evaluate the finite sample performances. Real data analysis is used to illustrate the proposed method. The results indicate that CDC-SIS performs well. [ABSTRACT FROM AUTHOR]

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an iterating principle in the bootstrap method to assess the individual bioequivalence under 2×4 randomized crossover design. The finite sample properties of the proposed algorithm are illustrated by an extensive simulation study and a real-world example. Our findings reveal that the proposed idea have better performance than the classical percentile bootstrap confidence limits. [ABSTRACT FROM AUTHOR]

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST). [ABSTRACT FROM AUTHOR]

Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has been widely recognized as a fundamental OFDM block transmission scheme because of its advantages in spectral efficiency. However, it suffers from severe performance loss under strong frequency selective channels and thus has difficulty supporting high-order modulations such as 256QAM. In this paper, we proposed a compressive sensing (CS)-based channel estimation (CE) algorithm for layer modulated TDS-OFDM (LM-TDS-OFDM). In the proposed CE algorithm, the low-order modulated symbols will be recovered and then sent to a feedback loop for precise CE. The priori-information hold subspace pursuit algorithm is investigated to achieve accurate estimation of channel. Analyses and simulations show that the proposed algorithm can successfully obtain high-accuracy CE and significantly reduce the computational complexity. Based on the proposed CE scheme, LM-TDS-OFDM can well support 256QAM transmission under severe channel conditions. [ABSTRACT FROM PUBLISHER]

This paper focuses on the lane departure avoidance system for a four in-wheel motors’ drive electric vehicle, aiming at preventing lane departure under dangerous driving conditions. The control architecture for the lane departure avoidance system is hierarchical. In the upper controller, the desired yaw rate was calculated with the consideration of vehicle-lane deviation, vehicle dynamic, and the limitation of road adhesion. In the middle controller, a sliding mode controller (SMC) was designed to control the additional yaw moment. In the lower layer, the yaw moment was produced by the optimal distribution of driving/braking torque between four wheels. Lane departure avoidance was carried out by tracking desired yaw response. Simulations were performed to study the effectiveness of the control algorithm in Carsim®/Simulink® cosimulation. Simulation results show that the proposed methods can effectively confine the vehicle in lane and prevent lane departure accidents. [ABSTRACT FROM AUTHOR]

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases. [ABSTRACT FROM AUTHOR]

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vectorized noncircular MUSIC (VNCM) algorithm based on the concept of the coarray, which can construct the difference and sum (diff–sum) coarray, for direction finding of the noncircular (NC) quasi-stationary sources. Utilizing both the NC property and the concept of the Khatri–Rao product, the proposed method can be applied to not only the ULA but also sparse arrays. In addition, we utilize the quasi-stationary characteristic instead of the spatial smoothing method to solve the coherent issue generated by the Khatri–Rao product operation so that the available degree of freedom (DOF) of the constructed virtual array will not be reduced by half. Compared with the traditional NC virtual array obtained in the NC MUSIC method, the diff–sum coarray achieves a higher number of DOFs as it comprises both the difference set and the sum set. Due to the complementarity between the difference set and the sum set for the coprime array, we choose the coprime array with multiperiod subarrays (CAMpS) as the array model and summarize the properties of the corresponding diff–sum coarray. Furthermore, we develop a diff–sum coprime array with multiperiod subarrays (DsCAMpS) whose diff–sum coarray has a higher DOF. Simulation results validate the effectiveness of the proposed method and the high DOF of the diff–sum coarray. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

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

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

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

In this paper, we explore the perspectives for canonical simulations in the worldline formulation of a lattice field theory. Using the charged field in two dimensions as an example, we present the details of the canonical formulation based on worldlines and outline the algorithmic strategies for canonical worldline simulations. We discuss the steps for converting the data from the canonical approach to the grand canonical picture which we use for cross-checking our results. The canonical approach presented here can easily be generalized to other lattice field theories with a worldline representation. [ABSTRACT FROM AUTHOR]

In this paper a simple four-point, in terms of time, but eight-value in total, identification method has been developed for the second-order linear Nomoto steering model. The algorithm intrinsically uses the zigzag test data in that it inherited some principles of the well-known procedure for the first-order model, from which it is essentially derived. The performance evaluation was then conducted with both simulated and real data. However, the results of these early, unprecedented efforts are far from satisfactory. Some potential sources of difficulties have been discussed. This calls for further research and improvement in order to provide a practical application of the method. [ABSTRACT FROM AUTHOR]

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient's biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design. [ABSTRACT FROM AUTHOR]

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

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

In this paper, we present a novel algorithm for peak-to-average power ratio (PAPR) reduction of OFDM signals by executing partial transmit sequence (PTS) in minimal trellis of block codes. The proposed method uses a linear code with good minimal trellis to select the transmitted OFDM signal with minimum PAPR and to transmit side information with error correction. The goals of combining strategy of trellis and PTS are twofold: to provide a low complexity PTS for weighting vector search and to highlight error protection capability for side information. Simulation results show that the proposed scheme achieves good PAPR reduction with lower complexity, compared to other PTS approaches. [ABSTRACT FROM PUBLISHER]

In this paper, a fast algorithm for the rapid calculation of the demagnetization field in micromagnetic modeling is presented. The method is applied to finite element micromagnetic modeling to study the switching phenomenon of perpendicular recording media. The method is shown to provide high efficiency in the numerical simulation with reduced memory requirement. With significant improvement in the computing speed, the practical application of recording physics based simulations can be extended to the investigation of perpendicular magnetic recording processes in various aspects, for instance, a large number of micromagnetic simulations can be carried out to build microtrack model and to predict the performance of perpendicular head and media combinations. [ABSTRACT FROM AUTHOR]

Reactive navigation is a well-known paradigm for controlling an autonomous mobile robot, which suggests making all control decisions through some light processing of the current/recent sensor data. Among the many advantages of this paradigm are: 1) the possibility to apply it to robots with limited and low-priced hardware resources, and 2) the fact of being able to safely navigate a robot in completely unknown environments containing unpredictable moving obstacles. As a major disadvantage, nevertheless, the reactive paradigm may occasionally cause robots to get trapped in certain areas of the environment—typically, these conflicting areas have a large concave shape and/or are full of closely-spaced obstacles. In this last respect, an enormous effort has been devoted to overcome such a serious drawback during the last two decades. As a result of this effort, a substantial number of new approaches for reactive navigation have been put forward. Some of these approaches have clearly improved the way how a reactively-controlled robot can move among densely cluttered obstacles; some other approaches have essentially focused on increasing the variety of obstacle shapes and sizes that could be successfully circumnavigated; etc. In this paper, as a starting point, we choose the best existing reactive approach to move in densely cluttered environments, and we also choose the existing reactive approach with the greatest ability to circumvent large intricate-shaped obstacles. Then, we combine these two approaches in a way that makes the most of them. From the experimental point of view, we use both simulated and real scenarios of challenging complexity for testing purposes. In such scenarios, we demonstrate that the combined approach herein proposed clearly outperforms the two individual approaches on which it is built. [ABSTRACT FROM AUTHOR]

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

The purpose of this paper is to study regional optimal control of a class of distributed bilinear systems in both cases of unbounded and bounded controls. Then, we prove the existence of an optimal control that minimizes a functional cost and we give characterization of such a control. Also we give condition that ensures the uniqueness of the optimal control. The obtained results lead to an algorithm that we illustrate by simulations for a bi-dimensional diffusion system excited by a zone actuator. [ABSTRACT FROM AUTHOR]

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms. [ABSTRACT FROM PUBLISHER]

The importance of Power Hardware-in-the-Loop (PHIL) experiments is rising more and more over the last decade in the field of power system and components testing. Due to the bidirectional exchange between virtual and physical systems, a true-to-reality interface is essential; however, linking several dynamic systems, stability issues can challenge the experiments, the components under test, and the individuals performing the experiments. Over the time, several interface algorithms (IA) have been developed and analyzed, each having different advantages and disadvantages in view of combining virtual simulations with physical power systems. Finally, IA are very specific to the kind of PHIL experiment. This paper investigates the operational range of several IA for specific PHIL setups by calculations, simulations, and measurements. Therefore, a selection of the mainly used respectively optimized IA is mathematically described. The operational range is verified in a PHIL system testing environment. Furthermore, in order to study the influence of different PHIL setups, according to software and hardware impedance, different tests using linear and switching amplifiers are performed. [ABSTRACT FROM AUTHOR]

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

Cross-efficiency evaluation is an effective method for ranking decision making units (DMUs) in data envelopment analysis, which is performed with peer-evaluation and self-evaluation. From different points of view, various cross-efficiency evaluations have been proposed with different secondary goals. Yet they usually lead to different average cross-efficiencies and different rankings. In this paper, we develop a concept of the aggressive game cross-efficiency, and propose an aggressive secondary model to minimize the cross-efficiencies of other DMUs under the constraints that the aggressive game cross-efficiency of the evaluated DMU is guaranteed. To achieve the aggressive game cross-efficiency, we develop an iterative algorithm. Mathematically, it is proved that all conventional average cross-efficiencies are sure to converge to the same aggressive game cross-efficiency by the iterative algorithm. Finally, numerical examples are presented to show the effectiveness of our approach in evaluating and ranking DMUs. [ABSTRACT FROM AUTHOR]

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

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

Telecommunication networks make extensive use of power amplifiers (PAs) to broaden the coverage from transmitter to receiver. Achieving high power efficiency is challenging and comes at a price: the wanted linear performance is degraded due to nonlinear effects. To compensate for these nonlinear disturbances, existing techniques compute the preinverse of the PA by the estimation of a nonlinear model. However, the extraction of this nonlinear model is involved and requires advanced system identification techniques. The plant inversion iterative learning control (ILC) algorithm is used here in combination with the best linear approximation to investigate whether the nonlinear modeling step can be simplified. This paper introduces the ILC framework for the preinverse estimation and predistortion of PAs. The ILC algorithm is used to obtain a high quality predistorted input for the PA under study without requiring a nonlinear model of the PA. In a second step, a nonlinear preinverse model of the amplifier is obtained. Both the nonlinear and memory effects of a PA can be compensated by this approach. The convergence of the iterative approach and the predistortion results are illustrated on a simulation of a Motorola LDMOS transistor-based PA and a measurement example using the Chalmers RF WebLab measurement setup. [ABSTRACT FROM PUBLISHER]

Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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

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

The performance of standard MVDR algorithm would be decreased sharply when the sampled data is small. In this paper, vector MVDR algorithm and the diagonal loading are proposed to improve the robustness. Firstly, the sample date covariance matrix is whitened to obtain more uniform noise, and then use the standard deviation of covariance matrix as the loading amount to complete diagonal loading. Finally, the effectiveness and robustness of the proposed algorithm is verified by simulation. Simulation results reveal that it has higher positioning accuracy with small sample, with the increase of SNR and snapshots, the estimation's RMSE of target location decreases continuously. [ABSTRACT FROM AUTHOR]

In this paper, a Shuffled Frog Leaping Algorithm (SFLA) is proposed to address the Profit-Based Unit Commitment (PBUC) problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi-constrained, nonlinear optimization problem. Some of the recently developed algorithms like GA, PSO, BBO, ACO, etc. are available to solve this complex problem, but so far there has been no such ideal technique which can completely handle the computational time and the large dimensionality of the problem. In this scenario, an attempt is made to solve this problem using SFLA. The problem is formulated as a biobjective optimization problem with profit maximization of generation companies as one objective and emission level limitation of generating units as second objective. The proposed algorithm is tested on IEEE 10 unit 10 hours load demand as input data for simulation using MATLAB. The results are compared with GA and PSO. From the results, it is observed that SFLA effectively handles the dimension of the problem and achieves maximum profit and minimum production cost with less computational time. [ABSTRACT FROM AUTHOR]

The asymmetric structure around the receiver provides a particular time delay for the specific incoming propagation. This paper designs a monaural sound localization system based on the reflective structure around the microphone. The reflective plates are placed to present the direction-wise time delay, which is naturally processed by convolutional operation with a sound source. The received signal is separated for estimating the dominant time delay by using homomorphic deconvolution, which utilizes the real cepstrum and inverse cepstrum sequentially to derive the propagation response's autocorrelation. Once the localization system accurately estimates the information, the time delay model computes the corresponding reflection for localization. Because of the structure limitation, two stages of the localization process perform the estimation procedure as range and angle. The software toolchain from propagation physics and algorithm simulation realizes the optimal 3D-printed structure. The acoustic experiments in the anechoic chamber denote that 79.0% of the study range data from the isotropic signal is properly detected by the response value, and 87.5% of the specific direction data from the study range signal is properly estimated by the response time. The product of both rates shows the overall hit rate to be 69.1%. [ABSTRACT FROM AUTHOR]

The symbiotic organisms search (SOS), which has been recently introduced, is a robust powerful metaheuristic global optimizer. This nature-inspired algorithm imitates the symbiotic interaction strategies in an ecosystem exercised by organisms involved in interrelationships to survive and reproduce. One of the main beneficial features of the SOS in contrast to many other competent metaheuristic algorithms is that the algorithm does not need any specific algorithm parameters or tuning process. This paper applies the SOS algorithm to simultaneously design optimal controllers of a power system equipped with both a power system stabilizer (PSS) and a thyristor-controlled series compensator (TCSC). The algorithm of SOS is utilized to concurrently tune the variables of controllers for both the PSS and TCSC in the nonlinear optimization process. Simulation results reveal that the optimal SOS-based coordinated controllers can significantly stabilize the system and efficiently damp oscillations under severe disturbances. Results will also show that the optimal controllers obtained perform slightly better than the optimal controllers obtained using the two commonly used global optimization solvers, the genetic algorithm (GA) and particle swarm optimization. [ABSTRACT FROM AUTHOR]

Haptics applications such as surgery simulations require collision detections that are more precise than others. An efficient collision detection method based on the clustering of bounding spheres was proposed in our prior study. This paper analyzes and compares the applied effects of the five most common subdivision surface methods on some 3D models for haptic collision detection. The five methods are Butterfly, Catmull-Clark, Mid-point, Loop, and LS3 (Least Squares Subdivision Surface). After performing a number of experiments, we have concluded that LS3 method is the most appropriate for haptic simulations. The more we applied surface subdivision, the more the collision detection results became precise. However, it is observed that the performance becomes better until a certain threshold and degrades afterward. In order to reduce the performance degradation, we adopted our prior work, which was the fast and precise collision detection method based on adaptive clustering. As a result, we obtained a notable improvement of the speed of collision detection. [ABSTRACT FROM AUTHOR]

For the current generation of cellular communication systems, long-term evolution (LTE) has been the major protocol to support high-speed data transmission. It is critical to allocate downlink spectral resource in LTE, namely, resource blocks (RBs), but the issue is not well addressed in the standard. Therefore, the paper develops an efficient RB allocation algorithm with 4 mechanisms to improve both fairness and throughput in LTE. For fairness concern, our RB allocation algorithm uses a resource-reservation mechanism to prevent cell-edge user equipments from starvation, and a credit-driven mechanism to keep track of the amount of resource given to each user equipment. For throughput concern, it adopts both weight-assignment and RB-matching mechanisms to allocate each RB to a packet according to its flow type and length. Through simulations, we demonstrate that the proposed RB allocation algorithm can significantly increase both throughput and fairness while reducing packet dropping and delays of real-time flows, as compared with previous methods. [ABSTRACT FROM AUTHOR]

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments. [ABSTRACT FROM AUTHOR]