Due to the disadvantage of the word segmentation tool in the aspect of the accuracy of word segmentation and speech tagging, it has harmful effects to the further research work. So this paper proposes a method based on the results of word segmentation to utilize language rules, which has been defined and described in Event Ontology, to verify and correct them. Experimental results show that compared with the method of only using word segmentation tool, the method of using language rules has a better performance.
Abstract Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm, the Double-ResNet Local Smoothing (DRLS) algorithm, is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method.
In recent years, more and more experts find that events have more semantic means than any other features, such as, characters, terms and concepts. This paper described a method which used event features to classify Chinese Web pages talking about personality in SVM classifier. Firstly, we explained how to represent Chinese documents by event features. Secondly, we constructed event ontology about personality information. We called this event ontology as Personality Event Ontology (PEOnt). Finally, an experiment was presented. This experiment showed that our approach results had an improved classification in terms of precision and recall. This improvement, however, come at a cost in a low features vector space's dimensionality due to the event features and event ontology used.
Mobile devices are leading the second revolution in the computer graphics arena, especially with regard to 3D graphics. The mobile phone is the most widespread mobile devices with rendering capabilities. Those capabilities have been very limited because the resources on such devices are relatively scarce with respect to the PC; small amounts of memory, little bandwidth, little chip area dedicated for special purposes, and limited power consumption. In order to enhance domestic research strength in the mobile graphics chips and break foreign patent blockade, we presents a fixed 3D graphics rendering pipeline for both geometry and rendering operation in this paper. The research of the 3D graphics rendering pipeline has gone through a rigorous design process: starting from system modeling (using Verilog HDL), algorithm validation on simulation platform, RTL implementation, and hardware/software co-simulation on FPGA development board. The maximum performance of the pipeline is 8.33M Polygons/s and 100M Pixels/s at an operating frequency of 100 MHz.
Pedestrian detection is a challenging task, due to wide variety of appearances, especially in complex real world scenes. The use of real-time pedestrian detection is of great use for a broad range of applications in multiple domains, such as surveillance and Intelligent Transportation System. In this paper we present a fast implementation of a robust pedestrian detector by using OpenCL, which is a novel open standard for heterogeneous computing. OpenCL allows for scalability to better performance and different types of hardware, with minimal changes to the implementation. To show the portable ability and performance of the new implementation of algorithm based on HOG, the algorithms are executed on the three different platforms, including CPU + NVidia GPU and CPU + AMD GPU heterogeneous system. By using a GPU as execution device, we exploit the data parallelism opportunities of the algorithm. In pedestrian detection, HOG is a very good algorithm but long running time because of its complexity. In this paper, the HOG and SVM algorithms will be optimized with OpenCL technology to achieve the goal of real-time requirements. On a single CPU + GPU machine, we reach 36 fps on the premise of algorithm's portability.
A non-contact method based on light reflection was developed to detect quantity-insufficiency information of the tablet counting machine in medicine packaging industry. The incident light beam coming from a detection unit illuminates the detection area. A detector embedded in the detection unit collects the reflected light rays. If one tablet-missed event happens, the detector will output intensive pulse signal. By analyzing the gathered signal, the detection device can recognize that there is not a tablet in current pit. High signal-to-noise ratio was observed in the experiment, so the recognition is readily accomplished. The detection device has been applied in practical industry. It reduces the omission rate low to one hundred-thousandth, far less than three thousandth under best vision monitoring by human.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)