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This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner–robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be $ 0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration.

Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.

This paper presents a nonlinear error feedback controller for a pneumatic soft bionic fin based on an extended state observer. Manufacturing process is shown for a bionic stingray with the pneumatic soft bionic fin. A test experiment is carried out for a driving part in the pneumatic soft bionic fin by upper chamber inflated and lower chamber inflated with different internal pressures. A dynamic nonlinear system is established for the pneumatic soft bionic fin by test experiment results and kinematic modeling. Total disturbances of the dynamic nonlinear system are estimated and compensated by the extended state observer and the nonlinear error feedback controller, respectively. Moreover, both the convergence of the extended state observer and stability of the dynamic nonlinear system are proved by Lyapunov approach. Finally, simulation results are shown to verify the effectiveness of the proposed control method for the pneumatic soft bionic fin.

A 3D measurement system consisting of a 3D scanner and an industrial robot (eye-in-hand) is commonly used to scan large object under test (OUT) from multiple fieldof-views (FOVs) for complete measurement. A data stitching process is required to align multiple FOVs into a single coordinate system. Marker-free stitching assisted by robot’s accurate positioning becomes increasingly attractive since it bypasses the cumbersome traditional fiducial marker-based method. Most existing methods directly use initial Denavit-Hartenberg (DH) parameters and hand-eye calibration to calculate the transformations between multiple FOVs. Since accuracy of DH parameters deteriorates over time, such methods suffer from high stitching errors (e.g., 0.2 mm) in long-term routine industrial use. This paper reports a new robot-scanner calibration approach to realize such measurement with low data stitching errors. During long-term continuous measurement, the robot periodically moves towards a 2D standard calibration board to optimize kinematic model’s parameters to maintain a low stitching error. This capability is enabled by several techniques including virtual arm-based robot-scanner kinematic model, trajectory-based robot-world transformation calculation, nonlinear optimization. Experimental results demonstrated a low data stitching error (< 0.1 mm) similar to the cumbersome marker-based method and a lower system downtime (< 60 seconds vs. 10-15 minutes by traditional DH and hand-eye calibration).

The spectroscopic properties of 2-μm emission in Ho 3+ -doped lanthanum–tungsten–tellurite glasses with different Ho 2 O 3 concentrations were investigated. The strongest emission was observed in a glass sample doped with 0.5 mol% Ho 2 O 3 , with a fluorescence lifetime of 3.16 ms and a emission cross section of 6.97×10 −21 cm 2 at 2040 nm. For this composition, no obvious changes in the emission properties and fluorescence lifetime were observed at temperatures of 295–350 K. Using this 60TeO 2 –30WO 3 –9.5La 2 O 3 –0.5Ho 2 O 3 glass as a core material, Ho 3+ -doped double cladding fiber with a core diameter of 14 μm and a numerical aperture of 0.12 was developed. A maximum laser output power of 34 mW at 2040 nm was achieved with a 9-cm-long Ho 3+ -doped tellurite glass fiber. To the best of our knowledge, this study presents the first report on the 2-μm laser output from a Ho 3+ -doped tellurite oxide glass fiber resonantly pumped by a 1.94 μm Tm-fiber laser.

Differential evolution (DE) is a simple yet efficient stochastic search approach for numerical optimization. However, it tends to suffer from slow convergence when tackling complicated problems. In addition, its search ability is significantly influenced by its control parameters. To improve the performance of the basic DE, this paper proposes a self-adaptive differential evolution with global neighborhood search (NSSDE). In the proposed NSSDE, its control parameters are self-adaptively tuned according to the feedback from the search process, while the global neighborhood search strategy is incorporated to accelerate the convergence speed. To evaluate the performance of the proposed NSSDE, we compare it with several DE variants on a set of benchmark test functions. The experimental results show that NSSDE can achieve better results than its competitors on the majority of the benchmark test functions.

This article presents the design modeling of a novel 3-RRS spherical parallel mechanism for ankle rehabilitation applications. The kinematics of the 3-RRS spherical parallel mechanism is established. The degree of freedom of 3-RRS spherical parallel mechanism is calculated using screw theory. The inverse kinematics of 3-RRS spherical parallel mechanism is solved. Eight groups of inverse solutions of 3-RRS spherical parallel mechanism are obtained. A method for forward position analysis is developed with variation and iteration approaches, which is suitable for motor position control. The ankle rehabilitation robot can be widely used in clinical treatment and can also be used at home, hotels, and fitness centers for ankle muscle relaxation.

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf’s boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

A highly Ho-doped single-mode lead germanate glass fiber is developed for compact ∼2 μm lasers. Over 600 mW of laser output operating at 2.04 μm is demonstrated in a 2 cm-long active fiber. The slope efficiency reaches ∼34.9% with respect to the launched 1.94 μm laser pump power. To the best of our knowledge, this is the highest laser output power demonstrated within a few centimeters of a Ho-doped fiber. This result shows that the fiber is highly promising for high-power single-frequency laser applications.