Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories

Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States

Neural decoding of visual object classification via functional magnetic resonance imaging (fMRI) data is challenging and is vital to understand underlying brain mechanisms. This paper proposed a multi-pooling 3D convolutional neural network (MP3DCNN) to improve fMRI classification accuracy. MP3DCNN is mainly composed of a three-layer 3DCNN, where the first and second layers of 3D convolutions each have a branch of pooling connection. The results showed that this model can improve the classification accuracy for categorical (face vs. object), face sub-categorical (male face vs. female face), and object sub-categorical (natural object vs. artificial object) classifications from 1.684% to 14.918% over the previous study in decoding brain mechanisms

Algorithms for Control of Welding Robotic-Manipulators Based on a Statistical Model of a Configuration Space

Предложены новые эффективные алгоритмы управления сварочными роботами-манипуляторами на основе статистической модели конфигурационного пространства. Предложенные алгоритмы, в отличие от известных, позволяют эффективно учесть ограничения на ориентацию технологического инструмента, сложную форму элементов роботизированного комплекса, а также ограничения, накладываемые на угловые скорости движения звеньев манипулятора. Эффективность разработанных алгоритмов подтверждается результатами тестирования в экспериментальной среде моделирования роботов.= The new effective algorithms were proposed for control of welding robotic-manipulators. The proposed algorithms are based on a statistical model of a configuration space. In contrast to known they effectively take into account complex shape of the elements of the robotic technological cell, the limits for the orientation of technological tool, as well as limits for the angular velocity of the manipulator links. The effectiveness of the proposed algorithms is confirmed by the tests in the experimental space of robot modeling

Search Trajectory of Robot Manipulators for Assembly and Welding in a Workspace With Obstacles

Рассматривается поиск траекторий сборочно-сварочных роботов-манипуляторов в рабочей среде с препятствиями. Предложены новые метод и алгоритмы поиска траекторий сборочно-сварочных роботов-манипуляторов на основе нейронной сети. Предлагаемый подход, в отличие от известных, позволяет эффективно учесть сложную форму элементов роботизированной технологической ячейки. Эффективность предложенного подхода подтверждается результатами моделирования.= the search for trajectories of assembly and welding robot manipulators in a working environment with obstacles is considered. a new method and algorithms for searching the trajectories of robot manipulators for assembly and welding based on a neural network are proposed. the proposed approach, unlike the known ones, allows to effectively take into account the complex shape of the elements of the robotic technological cell. the effectiveness of the proposed approach is confirmed by the results of modeling

SDK: A proposal of a general and efficient deterministic sampling sequence

Previous works have already demonstrated that deterministic sampling can be competitive with respect to probabilistic sampling in sampling-based path planners. Nevertheless, the definition of a general sampling sequence for any d-dimensional Configuration Space satisfying the requirements needed for path planning is not a trivial issue, over a multi-grid cell decomposition, of the ordering of the 2d descendant cells of any parent cell. This ordering is generated by the digital construction method using a d x d matrix Td. A general expression of this matrix (i.e. for any d) is introduced and its performance analyzed in terms of the mutual distance. The paper ends with a performance evaluation of the use of the proposed deterministic sampling sequence in different well know path planner

Exploration of the grasp space using independent contact and non-graspable regions

This report presents the use of independent contact and non-graspable regions to generate the grasp space for 2D and 3D discrete objects. The grasp space is constructed via a sampling method, which provides samples of force-closure or non force-closure grasps, used to compute regions of the graspable or non-graspable space, respectively. The method provides a reliable procedure for an efficient generation of the whole grasp space for n-finger grasps on discrete objects; two examples on 2D objects are provided to illustrate its performance. The approach has several applications in manipulation and regrasping of objects, as it provides a large number of force-closure and non force-closure grasps in a short time

RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT

SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides an accurate results. The focus about most SVM optimization research is the optimization of the the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with Particle Swarm Optimization (PSO) on SVM using the analysis of input data’s movement. This method performed the optimization of the weight of the input data and RBF kernel’s parameter at once based on the analysis of the movement of the input data which was separated from the process of determining the margin on SVM. The steps of this method were the parameter initialization, optimal particle search, kernel’s parameter computation, and classification with SVM. In the optimal particle’s search, the cost of each particle was computed using RBF function. The value of kernel’s parameter was computed based on the particles’ movement in PSO. Experimental result on Breast Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization method could improve the accuracy of SVM significantly. This method of RBF kernel optimization had a lower complexity compared to another SVM optimization methods that resulted in a faster running time