Synthesis and Optical Performances of a Waterborne Polyurethane-Based Polymeric Dye

A waterborne polyurethane-based polymeric dye (WPU-CFBB) was synthesized by anchoring 1, 4-bis(methylamino)anthraquinone (CFBB) to waterborne polyurethane chains. The number molecular weight, glass transition temperature, and average emulsion particle size for the polymeric dye were determined, respectively. This polymeric dye exhibited intriguing optical behaviors. The polymeric dye engendered two new absorption bands centered at about 520 nm and 760 nm if compared with CFBB in UV-vis spectra. The 760 nm peak showed hypsochromic shift with the decrease of average particle sizes. The polymeric dye dramatically demonstrated both hypsochromic and bathochromic effects with increasing temperature. The fluorescence intensity of the polymeric dye was much higher than that of CFBB. It was found that the fluorescence intensities would be enhanced from 20°C to 40°C and then decline from 40°C to 90°C. The fluorescence of the polymeric dye emulsion was very stable and was not sensitive to quenchers

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

The working accuracy of multi-linkage robot is seriously affected by the errors at the joints caused by the uncertainty factors such as vibration, wear, deformation, and manufacturing clearance. In order to improve the working accuracy, the joint motion prediction including these errors is researched, which can realize the follow-up errors pre-compensation. According to the spatial correlation and time dependence between the joints of the robot, the joints can be represented as a graph. This work proposes a joint trajectory prediction method based on graph convolutional neural network (GCN) and gated recurrent unit (GRU). A real experimental dataset is built to verify the effectiveness, including the uncertain errors. The method is validated by means of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Accuracy, and R-square. Experimental results demonstrate that the method obtains the highest performance in the joints trajectories prediction, compared with Historical Average (HA), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). In the case of Accuracy, the accuracy of the proposed method is 91.549%, which is 33.40%, 7.32% and 3.19% higher than that of HA, ARIMA and SVR, respectively. The method can effectively predict the joints trajectories of multi-linkage robot with uncertain error at the joint, and provide theoretical support for further error compensation, obstacle prediction, and obstacle avoidance control of robot

Conversion of Biomass-Derived Furfuryl Alcohol into Ethyl Levulinate Catalyzed by Solid Acid in Ethanol

A green and efficient process was developed for the conversion of biomass-derived furfuryl alcohol to ethyl levulinate using eco-friendly solid acid catalysts (zeolites and sulfated oxides) in ethanol. Studies for optimizing the reaction conditions such as the substrate concentration, the reaction time, the temperature, and the catalyst loading dosage were performed. With SO42−/TiO2 as the catalyst, a high ethyl levulinate yield of 74.6 mol% was achieved using a catalyst load of 5 wt% at 398 K for 2.0 h. The catalyst recovered through calcination was found to maintain good catalytic activity (47.8 mol%) after three cycles, and it was easily reactivated by re-soaking in H2SO4 solution. Catalyst characterization was based on BET surface area, NH3-TPD, and elemental analysis techniques

Characterisation of Terrain Variations of an Underwater Ancient Town in Qiandao Lake

The underwater ancient town of Chunan is of great importance in archaeology and tourism. Hence, the efficient mapping and monitoring of the topographical changes in this town are essential. An attractive choice for the efficient mapping of underwater archaeology is the multibeam echo sounder system (MBES). The MBES has several advantages including noncontact survey, high precision, and low cost. In this study, the topographical changes of the ancient town under Qiandao Lake were quantitatively assessed on the basis of time-series MBES data collected in 2002 and 2015. First, the iterative closest point (ICP) algorithm was applied to eliminate the coordinate deviations between two point sets. Second, the robust estimation method was used to analyse the characterisations of the terrain variations of the town on the basis of the differences between the two matched point sets. Obvious topographical changes ranging from −0.89 m to 0.88 m were observed in a number of local areas in the town. On the global scale, the mean absolute value of the depth change in the town was merely 0.12 m, which indicated a weak global deformation pattern. The experiment proved the effectiveness of applying MBES data to analyse the deformation of the ancient town. The results are beneficial to the study of underwater ancient towns and the development of protection strategies

Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition

In order to obtain the optimal perspectives of the recognition target, this paper combines the motion path of the manipulator arm and camera. A path planning method to find the optimal perspectives based on an A* algorithm is proposed. The quality of perspectives is represented by means of multi-view recognition. A binary multi-view 2D kernel principal component analysis network (BM2DKPCANet) is built to extract features. The multi-view angles classifier based on BM2DKPCANet + Softmax is established, which outputs category posterior probability to represent the perspective recognition performance function. The path planning problem is transformed into a multi-objective optimization problem by taking the optimal view recognition and the shortest path distance as the objective functions. In order to reduce the calculation, the multi-objective optimization problem is transformed into a single optimization problem by fusing the objective functions based on the established perspective observation directed graph model. An A* algorithm is used to solve the single source shortest path problem of the fused directed graph. The path planning experiments with different numbers of view angles and different starting points demonstrate that the method can guide the camera to reach the viewpoint with higher recognition accuracy and complete the optimal observation path planning

Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition

In order to obtain the optimal perspectives of the recognition target, this paper combines the motion path of the manipulator arm and camera. A path planning method to find the optimal perspectives based on an A* algorithm is proposed. The quality of perspectives is represented by means of multi-view recognition. A binary multi-view 2D kernel principal component analysis network (BM2DKPCANet) is built to extract features. The multi-view angles classifier based on BM2DKPCANet + Softmax is established, which outputs category posterior probability to represent the perspective recognition performance function. The path planning problem is transformed into a multi-objective optimization problem by taking the optimal view recognition and the shortest path distance as the objective functions. In order to reduce the calculation, the multi-objective optimization problem is transformed into a single optimization problem by fusing the objective functions based on the established perspective observation directed graph model. An A* algorithm is used to solve the single source shortest path problem of the fused directed graph. The path planning experiments with different numbers of view angles and different starting points demonstrate that the method can guide the camera to reach the viewpoint with higher recognition accuracy and complete the optimal observation path planning