Field of experts regularized nonlocal low rank matrix approximation for image denoising

The restoration of image degraded by noise is an essential preprocessing step for various imaging technologies. Nonlocal low rank matrix approximation has been successfully applied to image denoising due to the capability of recovering the underlying low rank structures. Unfortunately, existing rank minimization models ignore the correlation among image patches and their performance is degraded when encountering the heavy noise. To address this, we propose a field of experts regularized nonlocal low rank matrix approximation (RFoE) denoising model, which integrates a global field of experts (FoE) regularization, a fidelity term, and a nonlocal low rank constraint into a unified framework. The weighted nuclear norm is adopted as the low rank constraint while the FoE prior is utilized to capture the global structure information. An alternating direction minimization algorithm based on half quadratic splitting can effectively solve this model. Extensive experimental results demonstrate that the proposed RFoE model has a superior performance.</p

Fast actuator and sensor fault estimation based on adaptive unknown input observer

This study evaluates the robust fault estimation problem of systems with actuator and sensor faults though the simultaneous use of unknown input disturbances and measurement noise. Specifically, an augmented descriptor system is preliminarily developed by creating an augmented state consisting of system states and sensor faults. Next, a novel fast adaptive unknown input observer (FAUIO) is proposed for the system to enhance its fault estimation performance. The existence condition of the novel FAUIO is then introduced for linear time-invariant systems with unknown input disturbances. Furthermore, the proposed FAUIO is extended to a class of Lipschitz nonlinear systems with unknown input disturbances and measurement noise to investigate the robust fault estimation problem. Accordingly, an H&infin; performance index is employed to attenuate the influence of disturbances on fault estimation. Moreover, the linear matrix inequality (LMI) technique is applied to solve the designed FAUIO. Finally, the effectiveness of the developed FAUIO is validated via the simulation of two examples.</p

Single infrared image stripe removal via deep multi-scale dense connection convolutional neural network

Stripe noise removal is a crucial step for the infrared imaging system. Existing stripe removal methods are hard to balance stripe removal and image details preservation. In this paper, a deep multi-scale dense connection convolutional neural network (DMD-CNN) is proposed to address this problem. In DMD-CNN, a multi-scale feature representation unit (FR-Unit) is designed to decompose raw image into different scales which can extract diverse fine and coarse features. Dense connection is introduced into the network, which makes full use of the multi-scale information obtained by FR-Unit and avoids performance degradation. Moreover, the regularization term Lh is defined to depict the vertical direction smoothness property of stripe. Experiment results show that DMD-CNN performs more stable stripe removal effects in different scenes and diverse stripe intensity. Meanwhile, DMD-CNN outperforms seven state-of-the-art stripe removal methods on qualitative and quantitative evaluation

An optimal visual servo trajectory planning method for manipulators based on system nondeterministic model

When a manipulator captures its target by a visual servo system, uncertainties can arise because of mechanical system and visual sensors exist error. This paper proposes an intelligent method to predict the successful rate for a manipulator to capture its target with motion and sensor errors. Because the mapping between the joint space of the manipulator and the Cartesian space at the end of the manipulator is nonlinear, when there is a bounded error of the manipulator&#39;s joint, the error range of the end motion is constantly changing with the different joint positions. And at the same time, the visual servo camera will also measure the target from different positions and postures, so as to produce measurement results with different error ranges. The unknown time-varying error property not only greatly affects the stability of the closed-loop control but also causes the capture failure. The purpose of this paper is to estimate the success probability of different capture trajectories by establishing the nondeterministic model of manipulator control system. First, a system model including motion subsystem and feedback subsystem was established with system error described by Gaussian probability. And then Bayesian estimation was introduced into the system model to estimate the executing state of the predefined trajectory. Linear least quadratic regulators (LQR) control is used to simulate the input correction in the closed-loop control between motion subsystem and feedback subsystem. At last, the successful probability of capturing the target is established by the Gaussian distribution at the end point of the trajectory with geometric relationship calculation between tolerance range and error distribution. The effectiveness and practicability of the proposed method are proved by simulation and experiment.</p

一种基于多传感器数据融合算法的运动目标航迹感知方法

本发明涉及信息处理领域，具体说是一种基于多传感器数据融合算法的运动目标航迹感知方法。包括以下步骤：通过初始状态下的目标所在位置原点，以正东方向为X轴正向，根据右手定则建立绝对坐标系；将多种类型传感器采集的目标航迹信息转换为在绝对坐标系下目标航迹量测矩阵；多种类型传感器进行目标航迹信息匹配，对某种传感器采集目标的丢失情况进行处理，对多种类型传感器的目标航迹信息进行融合，得到融合后的目标航迹量测矩阵，即融合信息；将融合信息作为参数输入，利用最小二乘法进行数据融合，获取融合结果，并将融合结果添加至目标列表，得到目标航迹。本发明感知水面运动目标的信息精度高，且在机动时也可以保证对目标运动信息准确感知

Service Encapsulation Method Based on Industrial Internet

Based on the analysis of software and hardware integration and highly heterogeneous infrastructure in the traditional industrial field, this paper puts forward the concept of service packaging engine, puts forward a unified service packaging model for Industry by using the Internet thinking design mode, and can complete the control of complex industrial field equipment by editing only simple logic in the programming interface. This article focuses on basic service encapsulation

一种火炬对接无人机

本发明涉及无人机技术领域，特别涉及一种火炬对接无人机。包括旋翼无人机、机械臂、视觉引导系统及视觉定位系统，其中机械臂设置于旋翼无人机机身的下方，机械臂用于与目标火炬对接；视觉引导系统设置于旋翼无人机机身的正前方，视觉引导系统用于远距离检测目标火炬的位置；视觉定位系统设置于旋翼无人机的旋翼下方，视觉定位系统用于近距离检测目标火炬的位置。本发明可实现无人机在视觉引导系统引导下的大范围全自主火炬对接，机械臂在视觉定位系统驱动下的小范围精准火炬对接以及机械臂和火炬姿态不影响桨叶安全运行等功能

A non-interactive verifiable computation model of perceptual layer data based on CP-ABE

The computing of smart devices at the perception layer of the power Internet of Things is often insufficient, and complex computing can be outsourced to server resources such as the cloud computing, but the allocation process is not safe and controllable. Under special constraints of the power Internet of Things such as multi-users and heterogeneous terminals, we propose a CP-ABE-based non-interactive verifiable computation model of perceptual layer data. This model is based on CP-ABE, NPOT, FHE and other relevant safety and verifiable theories, and designs a new multi-user non-interactive secure verifiable computing scheme to ensure that only users with the decryption key can participate in the execution of NPOT Scheme. In terms of the calculation process design of the model, we gave a detailed description of the system model, security model, plan. Based on the definition given, the correctness and safety of the non-interactive safety verifiable model design in the power Internet of Things environment are proved, and the interaction cost of the model is analyzed. Finally, it proves that the CP-ABE-based non-interactive verifiable computation model for the perceptual layer proposed in this paper has greatly improved security, applicability, and verifiability, and is able to meet the security outsourcing of computing in the power Internet of Things environment

Carbon Black/PDMS Based Flexible Capacitive Tactile Sensor for Multi-Directional Force Sensing

Flexible sensing tends to be widely exploited in the process of human-computer interactions of intelligent robots for its contact compliance and environmental adaptability. A novel flexible capacitive tactile sensor was proposed for multi-directional force sensing, which is based on carbon black/polydimethylsiloxane (PDMS) composite dielectric layer and upper and lower electrodes of carbon nanotubes/polydimethylsiloxane (CNTs/PDMS) composite layer. By changing the ratio of carbon black, the resolution of carbon black/PDMS composite layer increases at 4 wt%, and then decreases, which was explained according to the percolation theory of the conductive particles in the polymer matrix. Mathematical model of force and capacitance variance was established, which can be used to predict the value of the applied force. Then, the prototype with carbon black/PDMS composite dielectric layer was fabricated and characterized. SEM observation was conducted and a ratio was introduced in the composites material design. It was concluded that the resolution of carbon sensor can reach 0.1 N within 50 N in normal direction and 0.2 N in 0-10 N in tangential direction with good stability. Finally, the multi-directional force results were obtained. Compared with the individual directional force results, the output capacitance value of multi-directional force was lower, which indicated the amplitude decrease in capacity change in the normal and tangential direction. This might be caused by the deformation distribution in the normal and tangential direction under multi-directional force

一种基于云边协同的配电台区分级线损分析系统

本发明提出的是一种基于云边协同的配电台区分级线损分析系统。包括云端线损分析平台、边端智能融合终端和电能数据采集终端。电能数据采集终端负责采集台区各个关键节点处的用电信息，并把采集的信息传送给边缘侧的智能融合终端，智能融合终端负责在边缘侧计算台区拓扑，并依据拓扑信息分级计算线损，云端的线损分析平台用于对边缘侧的拓扑信息进行校核以及依据边缘侧分级线损计算值，进行区域线损计算、异常线损点定位和窃电分析等线损精益化管理。本发明能够实现“变压器‑低压出线”、“低压出线与分支箱”、“分支箱与表箱”、“表箱与户表”的多级线损核算与分析方法，充分利用现有设备，成本低，易实现，从而实现配电台区的区域“自治”