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With the rapid growth of high-voltage transmission lines, the number of power transmission line equipments is correspondingly increasing. Power insulator is the basic component which plays the key role in the stable operation of power system. As a common defect of power insulators, missing-cap issue will affect the structural strength and durability of different power insulators. Therefore, the condition monitoring of power insulators is a daily but priority power line inspection task. Faced with the weak image features of small insulator defects in the aerial images, the conventional handcrafted features could not extract effectively powerful image features. Meanwhile, the small-scale insulator defects will bring a certain effect to the model training of deep learning. Therefore, the high-efficiency and accurate defect inspection still present a challenging task against complex backgrounds. To address the above issues, aimed at the missing-cap defects of power insulators, a novel defect identification algorithm from aerial images is proposed by taking advantage of state-of-the-art deep learning and transfer learning models. Fused with Spatial Pyramid Pooling (SPP) and MobileNet networks, a light deep convolutional neural network (DCNN) model based on You Only Look Once (YOLO) V3 network is proposed for fast and accurate insulator location to remove complex background interference. On the basis, combined with Dempster–Shafer (DS) evidence theory, the improved transfer learning model based on feature fusion is proposed for high-precision defect identification of power insulators. Experiments show that the proposed method could acquire a better identification performance against complex power inspection environment compared with other related detection models. [ABSTRACT FROM AUTHOR]

Due to the rapid development of social computerization and smart devices, there is an increasing demand for indoor positioning of mobile robots in the robotics field, so it is very important to realize the autonomous navigation of mobile robots. However, in indoor scenes, due to factors such as dark walls, the global positioning system cannot effectively locate, and the broadband and wired positioning technologies used indoors have problems such as base station laying and delay. Computer vision positioning technology has greatly improved the camera hardware due to its simple equipment and low cost. Compared with other sensor cameras, it is less affected by environmental changes, so visual positioning has received extensive attention. Image matching has become the most critical link in visual positioning. The accuracy, speed, and robustness of image matching directly determine the results of visual positioning, so image matching has become the main topic of this study. In this study, the neural network algorithm is systematically optimized, especially for the robot's local obstacle avoidance, and an obstacle data acquisition method based on VGG16 and fast RCNN is proposed. In order to solve the problem that the semantic image segmentation algorithm based on AlexNet and ResNet is difficult to accurately obtain the information of multiple objects, and an image semantic segmentation algorithm combined with VGG16 is designed to classify the background and road in the image at the pixel level and capture the path boundary line. The collection of robot obstacle path information improves the speed and accuracy of highly automated local obstacle avoidance. This study uses neural network algorithms to systematically optimize computer vision positioning and also studies the accuracy optimization of local obstacle avoidance, aiming to promote its better development. [ABSTRACT FROM AUTHOR]

Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm. [ABSTRACT FROM AUTHOR]

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imaging with a non-zero (forward) squint angle is capable of providing a longer time for reaction than that of the broadside mode. However, due to the large squint angle, there will be severe coupling between range and azimuth samples in the echoed data, which is known as the problematic Range Cell Migration (RCM) in the SAR community. Especially when the SAR sensor mounted on an airborne platform encounters unexpected motion deviations/errors, the coupling becomes more complicated, and it is difficult to differentiate the systematic RCM for the SAR Image Formation Processing (IFP) and the non-systematic RCM error to be compensated. To this end, a novel and accurate SAR imaging algorithm is proposed in this paper to facilitate the processing of airborne SAR data collected at a high-squint angle. Firstly, the proposed algorithm is established under a Fast Time-Domain Back-Projection (FTDBP) framework for the SAR IFP. FTDBP paves the way to avoid the complicated processing for the systematic RCM as for the conventional SAR IFP in the Doppler processing manner. It is capable of generating a high-resolution SAR image efficiently under more general geometries and configurations. Secondly, regarding the non-systematic RCM errors, the proposed algorithm realizes the compensation by correcting both the Non-systematic Range Cell Migration (NsRCM), as well as Azimuthal Phase Error (APE) in a coherent manner. It is consequently capable of auto-calibrating the effects of the motion error completely without being dependent on the airborne navigation unit. Finally, both simulated and raw data collected by the airborne squinted SAR are applied to evaluate the proposed algorithm. Comparisons with conventional algorithms are carried out to reveal the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The article presents careful comparisons among several empirical Bayes estimates to the precision parameter of Dirichlet process prior, with the setup of univariate observations and multigroup data. Specifically, the data are equipped with a two-stage compound sampling model, where the prior is assumed as a Dirichlet process that follows within a Bayesian nonparametric framework. The precision parameter α measures the strength of the prior belief and kinds of estimates are generated on the basis of observations, including the naive estimate, two calibrated naive estimates, and two different types of maximum likelihood estimates stemming from distinct distributions. We explore some theoretical properties and provide explicitly detailed comparisons among these estimates, in the perspectives of bias, variance, and mean squared error. Besides, we further present the corresponding calculation algorithms and numerical simulations to illustrate our theoretical achievements. [ABSTRACT FROM AUTHOR]

Implicit staggered-grid finite-difference (ISFD) scheme is competitive for its great accuracy and stability, whereas its coefficients are conventionally determined by the Taylor-series expansion (TE) method, leading to a loss in numerical precision. In this paper, we modify the TE method using the minimax approximation (MA), and propose a new optimal ISFD scheme based on the modified TE (MTE) with MA method. The new ISFD scheme takes the advantage of the TE method that guarantees great accuracy at small wavenumbers, and keeps the property of the MA method that keeps the numerical errors within a limited bound at the same time. Thus, it leads to great accuracy for numerical solution of the wave equations. We derive the optimal ISFD coefficients by applying the new method to the construction of the objective function, and using a Remez algorithm to minimize its maximum. Numerical analysis is made in comparison with the conventional TE-based ISFD scheme, indicating that the MTE-based ISFD scheme with appropriate parameters can widen the wavenumber range with high accuracy, and achieve greater precision than the conventional ISFD scheme. The numerical modeling results also demonstrate that the MTE-based ISFD scheme performs well in elastic wave simulation, and is more efficient than the conventional ISFD scheme for elastic modeling. [ABSTRACT FROM AUTHOR]

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, multi-user computation partitioning problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10 percent on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces. [ABSTRACT FROM AUTHOR]

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology. [ABSTRACT FROM AUTHOR]

The linearly constrained tensor n-rank minimization problem is an extension of matrix rank minimization. It is applicable in many fields which use the multi-way data, such as data mining, machine learning and computer vision. In this paper, we adapt operator splitting technique and convex relaxation technique to transform the original problem into a convex, unconstrained optimization problem and propose a fixed point iterative method to solve it. We also prove the convergence of the method under some assumptions. By using a continuation technique, we propose a fast and robust algorithm for solving the tensor completion problem, which is called FP-LRTC (Fixed Point for Low n-Rank Tensor Completion). Our numerical results on randomly generated and real tensor completion problems demonstrate that this algorithm is effective, especially for “easy” problems. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a new method to generate the random distribution of fibres in the transversal cross-section of fibre reinforced composites with high fibre volume fraction. The algorithm, named random sequential expansion (RSE) algorithm by the authors, is based on the hard-core model, but overcomes the jamming limit of hard-core model and is able to generate random distributions for high values of fibre volume fractions. By adjusting the inter-fibre distance parameters, it can generate microstructures with different fibre volume fractions expediently. Statistical analysis is performed on the generated distributions and a good agreement with the completely spatial random (CSR) pattern is found in all statistics analysed. Finite element analysis (FEA) is conducted to predict the effective properties of the generated microstructures. The predicted properties show reasonable agreement with the experimental results. The present algorithm is simple, highly efficient and provides a useful alternative to generate random numerical models that can be used in micromechanical analysis of composites. [Copyright &y& Elsevier]

Abstract: This paper will put forward a novel chaotic image encryption algorithm with confusion–diffusion architecture. First of all, secret keys will be processed by key generator before they can really be used in the encryption scheme, and in this stage this paper associates plain image with secret keys; Secondly, by imitating the trajectory of water wave movement, encryption algorithm will do scrambling operations to the image. Thirdly, this paper combines water drop motion and dynamic look up table to realize diffusion operations. For an 8 bits pixel, this algorithm will just dispose the higher 4 bits, which is because the higher 4 bits contain the vast majority of information of the image. At last, the experiment results and security analysis show that this proposed algorithm has a desirable encryption effect. Its key space is large enough, it is sensitive to keys and plain image, its encryption speed is fast and it can resist cryptanalysis such as brute attack, differential attack, etc. [Copyright &y& Elsevier]

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: A recently developed PLUS factorization holds great promise in image coding due to its simplicity and integer reversibility. However, existing PLUS factorizations did not consider stability and optimality. To address these problems, we propose methodologies to design stable and optimal PLUS factorization algorithms. Firstly, we propose three stable PLUS factorization algorithms, prove the stability theorem under no perturbation and analyze stability under perturbation. Furthermore, we obtain a closed-form formula for transform error, and use the formula to design an algorithm for optimal PLUS factorization. Then, we apply the PLUS factorization to image coding. The integer DCTs implemented with the optimal PLUS factorizations found by our algorithms outperform the integer DCT with expansion factors in terms of entropy. The optimal PLUS factorizations are superior to the lifting factorization in JPEG-XR. The experimental results agree with analytical results of PLUS factorization, and show superior performance of our algorithms in image coding. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a second order geometric iteration algorithm for point projection and inversion on parametric surfaces. The iteration starts from an initial projection estimation. In each iteration, we construct a second order osculating torus patch to the parametric surface at the previous projection. Then we project the test point onto the torus patch to compute the next projection and its parameter. This iterative process is terminated when the parameter satisfies the required precision. Experiments demonstrate the convergence speed of our algorithm. [Copyright &y& Elsevier]

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness. [ABSTRACT FROM AUTHOR]

This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy. [ABSTRACT FROM AUTHOR]

Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction. [ABSTRACT FROM AUTHOR]

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence. [ABSTRACT FROM AUTHOR]

The analysis of crosscut stability is an indispensable task in underground mining activities. Crosscut instabilities usually cause geological disasters and delay of the project. On site, mining engineers analyze and predict the crosscut condition by monitoring its convergence and stress; however, stress monitoring is time-consuming and expensive. In this study, we propose an improved extreme learning machine (ELM) algorithm to predict crosscut's stress based on convergence data, for the first time in literature. The performance of the proposed technique is validated using a crosscut response by means of the FLAC3D finite difference program. It is found that the improved ELM algorithm performs higher generalization performance compared to traditional ELM, as it eliminates the random selection for input weights. Furthermore, a crosscut construction project in an underground mine, Yanqianshan iron mine, located in Liaoning Province (China), is selected as the case study. The accuracy and efficiency of the improved ELM algorithm has been demonstrated by comparing predicted stress data to measured data on site. Additionally, a comparison is conducted between the improved ELM algorithm and other commonly used artificial neural network algorithms. [ABSTRACT FROM AUTHOR]

Most of the current algorithms used to solve the optimal configuration problem in the distributed generation (DG) of electricity depend heavily on control parameters, which may lead to local optimal solutions. To achieve a rapid and effective algorithm of optimized configuration for distributed generation, a hybrid approach combined with Bayesian statistical-inference and distribution estimation is proposed. Specifically, a probability distribution estimation model based on the theory of Bayesian inference is established, then a posteriori probability model with the prior distribution and the conditional distribution is generated, and new individual generators are formed into a dominant group. The information of each individual of this dominant group is used to update the probability model and the updated posteriori probability is used for sampling until the optimal solution is obtained. Finally, the 12 bus, 34 bus and 69 bus radial distribution system is used as an example and comparison is performed to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We propose a two-dimensional modified min-sum algorithm for the LDPC codes in the fifth generation (5G) networks standard to approach the error performance of the sum-product algorithm (SPA). In the proposed decoding algorithm, we adopt a partial self-correction method followed by message amplification to improve the reliability of the variable-to-check (V2C) messages. To further approach the performance of the maximum likelihood decoding for 5G short LDPC codes, we propose an enhanced quasi-maximum likelihood (EQML) decoding method. The proposed decoding method performs multiple rounds of decoding tests once the first decoding attempt fails, where the decoder inputs of the selected unreliable variable nodes are modified in each decoding test. A novel node selection method based on the sign fluctuation of V2C messages is proposed for the EQML decoding method. We also present a partial pruning stopping (PPS) rule to reduce the decoding complexity by deactivating part of the decoding tests once a valid codeword is found. A lower bound on the error performance is also derived by using the semi-analytical method. Simulation results show that the EQML decoding method outperforms the SPA with the same decoding complexity and other QML decoding methods, and it approaches the Polyanskiy-Poor-Verdú bound within 0.4 dB. [ABSTRACT FROM AUTHOR]

• A novel DWSSA-MPPT controller for each PV-Ćuk converter is proposed. • Multiple bidirectional PV-Ćuk converter system model is presented. • A state space model is developed for the PVCC and experimentally validated. • The control strategy for a PV system having more than one PVCC is discussed. • A simulated PV generator comprising three PV panels and two converters is verified. This paper presents a configuration scheme based multiple bidirectional Ćuk converter with a modified salp swarm algorithm (SSA) based maximum power point tracking (MPPT) for photovoltaic (PV) system under partial shading and module mismatching. The proposed scheme is made up of multiple bidirectional PV-Ćuk converter (PVCC) modules in a series chain, a novel SSA with dynamic nonlinear w factor (DWSSA) based the distributed maximum power point tracking (MPPT) and a terminal step-up converter for load connection. First of all, the configuration offers the advantage that the PVCC provides a current by pass and can also be controlled to allow two PV panels in the module to track their available maximum power under partial shading or module mismatching. In addition, the DWSSA-MPPT control scheme is used to estimate the voltages corresponding to the maximum power each PV panel can generate under its specific weather conditions. Moreover, a state space model is developed for the PVCC and experimentally validated, and the control strategy for a PV system having more than one PVCC is discussed. Finally, a simulated PV system based the proposed configuration scheme is set up and results are analyzed, which demonstrates that compared with some existing algorithms, the scheme with DWSSA-MPPT method can highly be effective under partial shading and module mismatching. [ABSTRACT FROM AUTHOR]

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes. [ABSTRACT FROM AUTHOR]

The distribution of the induced electric field (E-field) during transcranial magnetic stimulation (TMS) depends on the individual anatomical structure of the brain as well as coil positioning. Inappropriate stimulation may degrade the efficacy of TMS or even induce adverse effects. Therefore, optimizing the E-field according to individual anatomy and clinical need has become a research focus. In this paper, particle swarm optimization (PSO) was applied for the first time to the positioning of TMS coils with anatomical head models. We discuss the parameters of the PSO algorithm, which were optimized to achieve a reasonable convergence time suitable for in-time treatment planning. The optimizer improved the distribution of the induced E-field strength at the dedicated cortical region, with a mean value of 48.31% compared with that from the conventional treatment position. The optimization terminated after 4–11 iterations for 13 head models. The applicability and performance of the optimizer for a large population are discussed in terms of cortical complexity. This study could benefit not only clinics but also research on brain modulation. [ABSTRACT FROM AUTHOR]

Aiming at the problem of complex path planning in the processing of curved surface workpieces of body-in-white, a hybrid path planning method based on a memetic algorithm is proposed. The method is divided into two parts: welding sequence planning and welding path planning between weld points. By establishing the kinematic model of a spot welding robot based on the pipper criterion and z-y-z Euler angle solution method, the motion constraints of path optimization are analyzed. Under the framework of the memetic algorithm, the improved A-star algorithm with redundant node deletion and a post-smoothing process is used to obtain the smooth collision-free optimal path set between weld points and to construct the objective function of travelling all weld points with the shortest path length and highest smoothness. The multiobjective elitist-simulated annealing genetic algorithm (MESAGA) is used to achieve the welding sequence planning of all weld points. The variable neighborhood search method improves the mutation operator; the elitist strategy is introduced to improve the probability of elitist individual crossover and mutation operation, and a simulated annealing algorithm is used to jump out of local search to obtain the global optimal solution. According to the motion constraints, the joint space path is obtained by the optimal path in Cartesian space. Simulation analysis results demonstrate that the hybrid path planning method based on the memetic algorithm can effectively optimize the path of spot welding robots and lay the foundation for control and trajectory planning during welding processes. [ABSTRACT FROM AUTHOR]

Incremental learning strategy based feature selection approaches can improve the efficiency of reduction algorithm used for datasets with dynamic characteristic, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic interval-valued ordered data. Interval-valued ordered data is a generalized form of single-valued ordered data, which is more widely used in practice. However, the endpoints of the interval numbers are easily polluted by noise, thereby the knowledge granules are very sensitive. Motivated by these two issues, we study incremental feature selection approaches based on a fuzzy dominance neighborhood rough set (FDNRS) for dynamic interval-valued ordered data in this work. First, we propose the FDNRS model for an interval-valued ordered decision system (IvODS) and investigate its related properties. Second, a conditional entropy with robustness is proposed based on the proposed model. This conditional entropy can measure the degree of monotonic consistency of the IvODS, so it is used as a metric and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are proposed on the basis of the above researches. Experiments are performed on nine public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic IvODS. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of Internet of Things (IoT) devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber–physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this article, we consider the dynamic spectrum access in IIoT under a received signal strength-based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure. [ABSTRACT FROM AUTHOR]

• A practical ultra-high-speed transient based directional protection scheme for AC transmission lines connected to LCC-HVDC inverter station is proposed. • Ultra-high-speed. This paper only uses the 1 ms long data window, and the efficient and convenient SST is used for signal processing, so that the protection algorithm can be completed within 5 ms. • High reliability. This paper uses reliable transient energy as the protection criterion, and the efficient SST to achieve the protection algorithm, which solves the unreliable problem caused by mode aliasing and noise interference, etc. • Immune to commutation failure (CF). The proposed ultra-high-speed protection method can operate within 5 ms, avoiding the influence of CF, and all the simulations prove the reliability of the protection algorithm when CF is unavoidable. Commutation failure (CF) is a common phenomenon existing in the LCC-HVDC system, which threatens the normal operation of AC line protection near the inverter side. To solve this problem, this paper proposes an ultra-high-speed transient based directional protection method based on synchrosqueezing S transform (SST). Firstly, the frequency characteristics of the AC bus near the inverter side and the effect of CF on the action of the relay protection are analyzed in detail. Then, this paper analyzes the propagation mechanism of fault transient components by grid method, and proposes a protection criterion based on the transient energy ratio of forward and reverse current traveling wave fault components, which makes the protection scheme with higher reliability. To adapt to the wildly existed high frequency-low amplitude transient signals, this paper utilizes the novel SST for signal processing. SST takes advantage of S transform and synchrosqueezing wavelet transform, and it is more efficient and convenient. Based on the project parameters of the Yun-Guang LCC-HVDC system, a PSCAD simulation model is established. Extensive simulation tests and comparison with other algorithms verify the superiority of the proposed protection method under various fault conditions, as well as its immunity to CF. [ABSTRACT FROM AUTHOR]

Time-domain algorithms have significant performance advantages for missile-borne synthetic aperture radar (SAR) focusing with diving movement. However, due to the diving curve trajectory of the missile platform, the range and angular histories of the target become very sensitive to unknown tomography, which provides difficulties for SAR algorithm development. To address this problem, we have proposed a new fast factorized back-projection (FFBP) algorithm with reduced topography sensibility for missile-borne SAR focusing. The new algorithm was designed based on an orthogonal cylindrical coordinate (OCC) system, in which the cross section of a cylinder in the coordinate system is approximately orthogonal to the diving curve trajectory. Owing to the acquisition symmetry of the OCC system, the range and the angular histories of the grid in the OCC geometry become less dependent of the topography in every recursion of FFBP implementation, which can dramatically reduce the adverse effects of unknown topography and achieve high focusing performance. In the simulation, echo signal based on a set of typical parameters from a missile-borne SAR system is generated with unknown tomography. Promising results with 1 m resolution are finally achieved, which demonstrates the performance of the proposed algorithm. The limitation of the algorithm is also discussed in the final part, which will facilitate the development of raw data processes in practical application. [ABSTRACT FROM AUTHOR]

Swarm intelligence inspired algorithms have so many profound natural advantages in solving large-scale and distributed problems. This paper systematically analyzes the characteristics of wolves' behaviors such as cooperative searching, hunting and attacking, and further abstracts those behaviors into four basic ways, that is, wandering, summoning, lurking and besieging, in accordance with the different roles of wolves. Then, we formulate a cluster cooperative rule based on the principle of Dynamic Wolf Head Alternation and Real-time Role Assignment, and propose a fatigue-rendering tactics based on interception strategy in two teams. Finally, the clustering cooperative rule enlightened by the group's behavior is established, and the convergence of the algorithm is proved with the Markov asymptotic convergence theory. Experiments show that the model can effectively guarantee the efficiency of solving large-scale complex optimization problems and the operational effectiveness of distributed cluster cooperative attack problems. [ABSTRACT FROM AUTHOR]

Abstract: It is theoretically found that the boundary gas concentration could decline under transient due to the intergranular resolution. An efficient numerical method is first developed for the case before saturation. After the saturation of the grain boundary, a rate-release equation is applied not only to ensure no return of the gas released, but also ensure the gas conservation in the grain boundary. Accordingly, an efficient predict-correct algorithm for the intergranular gas concentration is invented for arbitrary power transient. Our numerical method has been validated by the analytical solution and the finite element solution. It demonstrates both high efficiency as well as high accuracy. [Copyright &y& Elsevier]