Vol 53, No 3 (2019)

An adaptive sliding mode controller based on fuzzy logic is proposed to control a manipulator robot over unknown surface trajectory using force-vision tracking, considering uncertainties of the kinematic, dynamic, and camera models. In this work we show that the robot can track the desired trajectories overcoming the model’s uncertainties, the use of the sliding mode to reject the disturbances and converge much faster, a nonlinear sliding surface proposed to regulate the convergence speed in order to illuminate the overshoot of the system response, thanks to the online fuzzy logic adaption, used to generate the equivalent control. The system’s stability has been validated using Lyapunov criteria. So as to show the performance of the proposed control law, we performed simulations consisting of a series of tests in various conditions. The obtained results allowed us to validate the robustness of the controller towards the payload variations and the model’s uncertainties.

This paper provides a new technique for constructing unbiased statistical prediction limits on order statistics of future samples using the results of a previous sample from the same underlying inverse Gaussian distribution. Statistical prediction limits for the inverse Gaussian distribution are obtained from a classical frequentist viewpoint. The results have direct application in reliability theory, where the time until the first failure in a group of several items in service provides a measure of assurance regarding the operation of the items. The statistical prediction limits are required as specifications on future life for components, as warranty limits for the future performance of a specified number of systems with standby units, and in various other applications. Prediction limit is an important statistical tool in the area of quality control. The lower prediction limits are often used as warranty criteria by manufacturers. The technique used here does not require the construction of any tables. It requires a quantile of the beta distribution and is conceptually simple and easy to use. The discussion is restricted to one-sided tolerance limits. For illustration, a numerical example is given.

With the acceleration of urbanization, urban traffic problems are becoming more and more prominent. In the face of massive traffic data, it is difficult to predict traffic condition with effective data analysis methods. In order to deal with traffic data better, this study applied data mining in traffic data analysis and processing, constructed a Hadoop based data analysis system to collect and preprocess data, and analyzed traffic data using parallel distributed calculation based on MapReduce. The improved fuzzy c-means (FCM) algorithm and the random forest algorithm were used. The simulation results showed that the error rate of the improved FCM algorithm is 10% and the accuracy rate of the random forest algorithm is 92.3%, indicating the system had high reliability. Then an experiment was carried out on the main traffic roads in Huadu district of Guangzhou, China. It was found that the method was efficient and accurate and had a good application prospect.

Since the publication of the High Efficient Video Coding (HEVC) as the newest video coding standard, some extension of HEVC has been explored. Intra Block Copy (IntraBC) is one of the most important tools defined in screen coding extension of HEVC. However, the simplex partitioning method used in IntraBC leads to the problem that one CU may contain several irrelevant and complex texts or graphs, which degrades the coding efficiency. In this paper, an improved Intra block copy algorithm is proposed based on character segmentation exclusively for screen videos containing a large number of texts. Experimental results show that the proposed algorithm can provide a Bjøntegaard delta bit rate (BD-rate) reduction of 0.72% with only 3% increase in encoding time and no increase in decoding time compared with the default algorithm in HM-16.7+SCM-5.4.

For the incomplete scanning data, the traditional algorithms cannot guarantee that the medical CT reconstruction image meets the diagnostic requirements. In this case, a medical CT image reconstruction method with the limited angle projection is proposed. According to the theory of compressed sensing, medical CT images with a sparse representation can be reconstructed from the incomplete scanning data and provide reliable information for the diagnosis. The sparse representation of CT images is performed by the sparse patch-ordering wavelet-tree transform, and the digital features of sparse coefficients are used as regularization terms to ensure the validity of the solution. Meantime, the weighting term is added into the fidelity item to reduce the influence of noise on the reconstruction results. The extended Lagrange method is used to solve the constrained objective function iteratively so as to realize the reconstruction of low dose medical CT images. Simulation results demonstrate that the reconstructed image can not only satisfy the completeness condition of projection data, but also can reconstruct the high quality image and effectively improve the mean square error and the structural similarity index.