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This study aims to develop a new kind of functional low molecular weight organic dyes, which is highly efficient, meanwhile inexpensive and easily prepared and modified and can be used in photoacoustic (PA) imaging and photothermal therapy (PTT). To further realize the release of molecules under the biomedical condition, the releasing efficiency of micellar nanoparticles under different stimuli were represented.

A class of azo and Schiff base derivatives with different click reagents were characterized by PA imaging and photothermal (PT) experiments. The molecule with best PT effect was loaded into a temperature-stimuli-sensitive amphiphilic block copolymer which demonstrated the capability of releasing the polymers under the near-infrared (NIR) light of 650 nm.

The PA and PT effects of a series of azo and Schiff base derivatives with different click reagents were characterized. Introducing the click reagent F4-TCNQ can result in red shift of peaks of PA intensity. Stimulated with 650 nm laser irradiation, the polymer processed higher release rate than being stimulated by temperature stimuli.

This paper not only guides the design of NIR dyes with good PA intensity but also provides a method which has great potential for the application of NIR photothermal dyes in the field of biotechnology for controlled release.

This paper uses click reagents to modify azo and Schiff derivatives and an amphiphilic block copolymer under NIR light to realize controlled release.

This paper aims to present a multiscale fuzzy optimization (FO) method to optimize both the density distribution and macrotopology of a uniform octet-truss lattice structure.

The design formulae for the strut radii are presented based on the effective mechanical properties obtained from the representative volume element. The proposed basic lattice material is applied in a normalization process to determine the material model with penalization. The solid isotropic material with penalization (SIMP) method is extended to solve the minimum compliance problem using the optimality criteria. The evolutionary deletion process is proposed to delete elements corresponding to thin-strut unit cells and to obtain the optimal macrotopology.

Both numerical cases indicate that the FO results significantly improved in structural performance compared with the results of the conventional SIMP. The deleting threshold controls the macrotopology of the graded-density lattice structures with negligible effects on the mechanical properties.

This paper presents one of the first multiscale optimization methods to optimize both the relative density and macrotopology of uniform octet-truss lattices. The material model and corresponding optimality criteria of octet-truss lattices are proposed and implemented in the optimization.

The sensorless external force estimation of robot manipulator can be helpful for reducing the cost and complexity of the robot system. However, the complex friction phenomenon of the robot joint and uncertainty of robot model and signal noise significantly decrease the estimation accuracy. This study aims to investigate the friction modeling and the noise rejection of the external force estimation.

A LuGre-linear-hybrid (LuGre-L) friction model that combines the dynamic friction characteristics of the robot joint and static friction of the drive motor is proposed to improve the modeling accuracy of robot friction. The square root cubature Kalman filter (SCKF) is improved by integrating a Sage Window outer layer and a nonlinear disturbance observer (NDOB) inner layer. In the outer layer, Sage Window is integrated in the square root Kalman filter (W-SCKF) to dynamically adjust noise statistics. NDOB is applied as the inner layer of W-SCKF (NDOB-WSCKF) to obtain the uncertain state variables of the state model.

A peg-in-hole contact experiment conducted on a real robot demonstrates that the average accuracy of the estimated joint torque based on LuGre-L is improved by 4.9% in contrast to the LuGre model. Based on the proposed NDOB-WSCKF, the average estimation accuracy of the external joint torque can reach up to 92.1%, which is improved by 4%–15.3% in contrast to other estimation methods (SCKF and NDOB).

A LuGre-L friction model is proposed to handle the coupling of static and dynamic friction characteristics for the robot manipulator. An improved SCKF is applied to estimate the external force of the robot manipulator. To improve the noise rejection ability of the estimation method and make it more resistant to unmodeled state variable, SCKF is improved by integrating a Sage Window and NDOB, and a NDOB-WSCKF external force estimator is developed. Validation results demonstrate that the accuracy of the robot dynamics model and the estimated external force is improved by the proposed method.

This paper aims to present an adaptive robust sliding mode tracking controller for a 6 degree-of-freedom industrial assembly robot with parametric uncertainties and external disturbances. The controller is used to achieve both stringent trajectory tracking, accurate parameter estimations and robustness against external disturbances.

The controller is designed based on the combination of sliding mode control, adaptive and robust controls and hence has good adaptation and robustness abilities to parametric variations and uncertainties. The unknown parameter estimates are updated online based on a discontinuous projection adaptation law. The robotic dynamics is first formulated in both joint spaces and workspace of the robot’s end-effector. Then, the design procedure of the adaptive robust sliding mode tracking controller and the parameter update law is detailed.

Comparative tests are also conducted to verify the effectiveness of the proposed controller, which show that the proposed controller achieves significantly better dynamic trajectory tracking performances as compared with conventional proportional derivative controller and sliding mode controller under the same conditions.

This is a new innovation for industrial assembly robot to improve assembly automation.

The work efficiency and energy consumption of astronauts in the space station are the key issues in the operation of the space station, and how to evaluate the lean value of their activities is also complex and abstract. Combined with the idea of lean management, this paper aims to propose an space station dynamic value stream mapping system that can monitor and continuously improve the value flow and energy flow of astronauts in real time through lean methods.

Through systematic literature review, it is found that there is little research on the issue of lean management for astronauts. In manufacturing and services, value stream mapping is widely used for lean management. However, the static value stream map lacks the characteristics of real-time dynamics. This paper proposes to take the three modules of Muda detection, action recognition and energy monitoring as the basic content of the astronaut lean management framework to make the value stream and energy stream dynamic.

The theoretical framework of astronaut lean management is initially constructed, and the reasons for astronaut Muda and improvement ideas are also analyzed.

In fact, practitioners can use the proposed framework to identify the value of astronauts. Academically, these results collect research on dynamic value stream and lean management, providing a new way to identify value in aerospace using lean methods. Finally, the future research goals of astronaut lean management are put forward.

This paper aims to evaluate the efficiency and mechanism of three kinds of expired nitroimidazole antibiotics as corrosion inhibitor for mild steel in 1M HCl.

Evaluation was carried out by weight loss and electrochemical techniques. The surface morphology and the composition of the elements of adsorption layer are studied by scanning electron microscopy and energy dispersive spectrometer.

The experimental results reveal that the maximum value of inhibition efficiency appear at an extreme point of concentration with the increase of concentration of the inhibitors. Ornidazole has better corrosion inhibition than metronidazole but not as tinidazole. The inhibitors all act as cathodic type corrosion inhibitor. The adsorption of ornidazole, metronidazole and tinidazole on mild steel obeys Langmuir adsorption isotherm and belongs to chemisorption of electron donating. Combined with the molecular structure of the corrosion inhibitor and the experimental structure, the authors propose a detailed mechanism analysis.

The expired nitroimidazole antibiotics as corrosion inhibitor for mild steel in hydrochloric acid solution is first studied. It provides a way to deal with expired drugs, thereby reducing environmental pollution. The study explored the inhibition mechanism affecting by comparison different structure of three kinds of expired nitroimidazole antibiotics molecular, providing theoretical support for the preparation of the new inhibitor.

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

This paper aims to achieve automatically surface segmentation for painting different kinds of aircraft efficiently considering the demands of painting robot.

This project creatively proposed one method that accepts point cloud, outputs several blocks, each of which can be handled by ABB IRB 5500 in one station. Parallel PointNet (PPN) is proposed in this paper for better handling six dimensional aircraft data including every point normal. Through semantic segmentation of PPN, each surface has its own identity information indicating which part this surface belongs to. Then clustering considering constraints is applied to complete surface segmentation with identity information. To guarantee segmentation paintable and improve painting efficiency, different dexterous workspaces of IRB 5500 corresponding to different postures have been analyzed carefully.

The experiments confirm the effectiveness of the proposed surface segmentation method for painting different types of aircraft by IRB 5500. For semantic segmentation on aircraft data with point normal, PPN has higher precision than PointNet. In addition, the whole algorithm can efficiently segment one complex aircraft into qualified blocks, each of which has its own identity information, can be painted by IRB 5500 in one station and has fewer edges with other blocks.

As the provided experiments indicate, the proposed method can segment one aircraft into qualified blocks automatically, which highly improves the efficiency in aircraft painting compared with traditional approaches. Moreover, the proposed method is able to provide identity information of each block, which is necessary for application of different paint parameters and different paint materials. In addition, final segmentation results by the proposed method behaves better than k-means cluster on variance of normal vector distance.

Inspired by semantic segmentation of 3 D point cloud, some improvements based on PointNet have been proposed for better handling segmentation of 6 D point cloud. By introducing normal vectors, semantic segmentation could be accomplished precisely for close points with opposite normal, such as wing upper and lower surfaces. Combining deep learning skills with traditional methods, the proposed method is proved to behave much better for surface segmentation task in aircraft painting.

This paper aims to present an image-based visual servoing algorithm for a multiple pin-in-hole assembly. This paper also aims to avoid the matching and tracking of image features and the remaining robust against image defects.

The authors derive a novel model in the set space and design three image errors to control the 3 degrees of freedom (DOF) of a single-lug workpiece in the alignment task. Analytic computations of the interaction matrix that link the time variations of the image errors to the single-lug workpiece motions are performed. The authors introduce two approximate hypotheses so that the interaction matrix has a decoupled form, and an auto-adaptive algorithm is designed to estimate the interaction matrix.

Image-based visual servoing in the set space avoids the matching and tracking of image features, and these methods are not sensitive to image effects. The control law using the auto-adaptive algorithm is more efficient than that using a static interaction matrix. Simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed algorithm.

This paper proposes a new visual servoing method to achieve pin-in-hole assembly tasks. The main advantage of this new approach is that it does not require tracking or matching of the image features, and its supplementary advantage is that it is not sensitive to image defects.

In view of the defects of glued wood beams, a new composite member – reconstituted bamboo board reinforced glued wood beams is proposed to improve the bearing capacity of glued wood beams.

The bending test studied the ordinary glulam beams and the reinforced glulam beams with different layer numbers and different layer thicknesses by comparing with six kinds of glulam beams strengthened with bamboo scrimber and one kind of ordinary glulam beams and used the method of third-point stepwise loading on the glulam beams strengthened with bamboo scrimber.

The bamboo scrimber improved the bending behavior of the ordinary glulam beams. The 10 mm bamboo scrimber layer can meet the requirements of the maximum ultimate bending capacity and minimize the defects. So 10 mm bamboo scrimber layer was the optimal thickness. During the loading process, the strain change of the normal section of the reconstituted bamboo board reinforced glued wood beam basically conforms to the plane section assumption.

The bending rigidities of the glulam beams strengthened with bamboo scrimber increased up to 28.25%, 8.53% and 76.67%, and the ultimate bending capacity increased from 83.44% to 99.34% with the increase of the bamboo scrimber plate layers (the replacement rate). The ultimate bending capacities and the bending rigidities of the glulam beams strengthened with bamboo scrimber increased to 52.32%∼60.18% and 90.07%∼99.34% with the changing of the bamboo scrimber thicknesses from 7.1 mm to 25 mm.