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FinSBD-3 is a shared task organized in the context of the 1st workshop on Financial Technology on the Web. The task focuses on extracting the entire structure of noisy PDF financial documents that include 1) sentences, lists, items, and organization of lists and items; 2) figures and tables; 3) headers and footers. This paper describes the approach that allows us to extract the figures and tables using their visual cues. We applied the object segmentation techniques in image processing to detect the location of figures and tables in the PDF files. A post-processing method is then executed in order to find exact content. The result shows the potential of this approach.

This paper proposes a combination between a neural network and an adaptive sliding mode control for trajectory tracking control of a 3-DOF planar parallel manipulator. It has a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. The proposed control algorithm is to use a PID sliding mode surface, an adaptive sliding mode controller with a neural network to overcome the drawback of the traditional sliding mode controllers, such as slow response rate with variation of uncertainties and external disturbances, chattering, and upper bound values of undefined dynamics which affects system performance, high wear of moving mechanical parts and high heat losses in power circuits. The radial basis function neural network is designed to compensate for uncertainties and external disturbances, which allows small switching gain. Hence, the chattering can be significantly reduced. In addition, an adaptive control law is used to adaptively converge small switching gains of the sliding mode controller as the neural network reduces model uncertainties. The effectiveness of the proposed control strategy is demonstrated by simulations which are conducted by using the combination of Sim-Mechanics and SolidWorks.

This paper proposes an adaptive integral sliding mode tracking control for robotic manipulators. Our proposed control method is developed based on the benefits of both integral sliding mode control and adaptive control, such as high robustness, high accuracy, and estimation ability. In this paper, an integral sliding mode controller is designed with the elimination of the reaching stage to provide better trajectory tracking accuracy and to stabilize the closed-loop system. To reduce the computation complexity, an adaptive controller with only one simple adaptive law is used to estimate the upper-bound values of the lumped model uncertainties. As a result, the requirement of their prior knowledge is eliminated and then decrease the computation cost. Consequently, this controller provides better tracking accuracy and handles the dynamic uncertainties and external disturbances more strongly. The system global stability of the controller is guaranteed by using Lyapunov criteria. Finally, the effectiveness of the proposed control method is tested by computer simulation for a PUMA560 robotic manipulator.

Control of parallel manipulators is a challenging and difficult task. In this paper, we propose a new adaptive synchronized sliding mode controller for planar parallel manipulators by using fuzzy logic system. The proposed controller is based on the combination of the definition of synchronized control, sliding mode control and online self-tuning fuzzy logic system. Firstly, the dynamic model of parallel manipulators is presented in active joint space. Based on this dynamic model, a synchronized sliding mode controller is developed. And then, in order to compensate the uncertainties of the control system, an online self-tuned fuzzy logic system is adopted. The self-tuning law is proposed by using the cross-coupling error of the parallel manipulator. The stability of the closed loop system is guaranteed by using Lyapunov theory. The simulations were conducted on Matlab/Simulink to verify the effectively of the proposed control algorithm.