Physical layer security of underlay cognitive radio using maximal ratio combining

We investigate the secrecy outage performance of maximal ratio combining (MRC) in cognitive radio networks over Rayleigh fading channels. In a single-input multiple-output wiretap system, we consider a secondary user (SU-TX) that transmits confidential messages to another secondary user (SU-RX) equipped with M (M ≥ 1) antennas where the MRC technique is adopted to improve its received signal-to-noise ratio. Meanwhile, an eavesdropper equipped with N (N ≥ 1) antennas adopts the MRC scheme to overhear the information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the service quality of the primary user without spectrum sensing. We derive the closed-form expressions for an exact and asymptotic secrecy outage probability.     

基于离差最大化组合赋权的煤矿安全评价研究

针对组合赋权中权重分配不够客观准确的问题,提出基于离差最大化组合赋权的煤矿安全评价法.综合分析煤矿安全生产的风险原因,建立包含12个一级指标、50个二级指标的评价体系.采用熵权法与变异系数法计算各指标的客观权重,通过构建的离差最大化组合预警评价模型使两种赋权法的组合结果最佳,利用Spearman秩相关系数法检验其兼容度...     

新型SPLL在蓄电池充放电装置中的应用研究

叙述了双同步旋转坐标变换原理,提出了一种基于此变换的新型锁相环(SPLL)设计方法,阐述了新型SPLL在蓄电池充放电装置中的具体应用方案,该方案能够在电网电压发生畸变及不平衡条件下利用双同步旋转坐标变换快速、准确地锁定电网电压相位。根据该应用方案建立的装置控制策略将大大提高基于可逆PWM整流器的蓄电池充放电装置的充放电性能和效率。仿真结果验证了方案的可行性和有效性。     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

Asynchronous Switching Control of Discrete-time Linear System Based on Mode-dependent Average Dwell Time

International Journal of Control, Automation and Systems - In this paper, we investigate the problem of asynchronous switching control for discrete-time linear systems with mode-dependent average...     

A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation

Image segmentation plays an important role in the computer vision . However, it is extremely challenging due to low resolution, high noise and blurry boundaries. Recently, region-based models have been widely used to segment such images. The existing models often utilized Gaussian filtering to filter images, which caused the loss of edge gradient information. Accordingly, in this paper, a novel local region model based on adaptive bilateral filter is presented for segmenting noisy images. Specifically, we firstly construct a range-based adaptive bilateral filter, in which an image can well be preserved edge structures as well as resisted noise. Secondly, we present a data-driven energy model, which utilizes local information of regions centered at each pixel of image to approximate intensities inside and outside of the circular contour. The estimation approach has improved the accuracy of noisy image segmentation. Thirdly, under the premise of keeping the image original shape, a regularization function is used to accelerate the convergence speed and smoothen the segmentation contour. Experimental results of both synthetic and real images demonstrate that the proposed model is more efficient and robust to noise than the state-of-art region-based models.

     

Fixed-time Output Feedback Consensus of Second-order Multi-agent Systems with Settling Time Estimation

This study discusses fixed-time consensus problem of second-order multi-agent systems with unmeasur-able velocity and uncertain disturbance. The proposed c     

A Robust Control Approach for Hydraulic Excavators Using <Emphasis Type="Italic">μ</Emphasis>-synthesis

In this work, a robust control is applied to the automation of a hydraulic excavator. Hydraulic excavators exhibit complex nonlinear behavior due to the inherent nonlinearity of the hydraulic servo system. Furthermore, the hydraulic excavator is subject to large disturbance forces during interaction with the environment. As a result, conventional feedback control techniques, such as a proportional-integral-derivative (PID) control, fail to provide consistent performance over the whole operation region of the excavator. Especially, when phase-offset errors vary between joints, undesirable motions are generated in the workspace, which evidently degrades the overall performance of the controller. With this in mind, we apply a robust control approach to the autonomous hydraulic excavators. By handling the nonlinearities and disturbances as uncertainties within the joint dynamics, a robust controller is designed by means of μ-synthesis that guarantees robust stability and performance within the given uncertainty bounds. Furthermore, by adopting a common model reference for each joint, we seek to increase the overall performance of tracking the digging trajectory in the workspace. Experimental results of the robust controller conducted on an industrial 21-ton class hydraulic excavator are presented.     

An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.