This work concerns the assessment of mold parts with multi-view scanning point cloud data. With the soaring demand for complex geometry parts, multi-axis electrical discharge machining (EDM) manufacturing also faces the challenge of high-cost and the requirement of high-accuracy. As a part of EDM, well-judged life-time and wear condition of mold parts are critical to ensuring product quality. In this work, we present a novel non-contact surface inspection solution for mold part 3D reconstruction and inspection based on light-section scanning. After calibration and registration, multi-view stereo (MVS) point cloud data for the parts can be obtained, which can then be used to generate 2D section profiles. Comparison method is designed to examine if the design tolerance is met by comparing the section profiles against those based on the CAD model of the parts. Our method is verified by assessing worn electrode parts, which are commonly used in machining centers. The method proposed in thi
In this article, an advanced nonlinear control scheme is proposed for a three-phase grid-connected inverter and a solar photovoltaic (PV) system connected dc–dc converter. The proposed control scheme is based on a nonlinear adaptive integral backstepping approach. The robustness of the proposed control scheme is achieved through maintaining stability of the overall system, extacting maximum power from the solar PV, and controlling active power. Moreover, the injection of less harmonic components into the grid, the superior performance against different atmospheric conditions, and external disturbances enduring capability are also key features of the controller. The stability of the overall system is achieved through the negative-definite control Lyapunov functions of the proposed control scheme. An adaptive law of the proposed control scheme on external disturbances provides the undergoing capability against the external disturbances. Maximum dc power from the solar PV system is obta
Abstract
In clinics, the information about the appearance and location of brain tumors is essential to assist doctors in diagnosis and treatment. Automatic brain tumor segmentation on the images acquired by magnetic resonance imaging (MRI) is a common way to attain this information. However, MR images are not quantitative and can exhibit significant variation in signal depending on a range of factors, which increases the difficulty of training an automatic segmentation network and applying it to new MR images. To deal with this issue, this paper proposes to learn a sample-adaptive intensity lookup table (LuT) that dynamically transforms the intensity contrast of each input MR image to adapt to the following segmentation task. Specifically, the proposed deep SA-LuT-Net framework consists of a LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific nonlinear intensity mapping function through communication with the segmentation mo
Abstract
Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in Global Position System (GPS) friendly environments, in GPS denied environments (such as suburb, tunnel, forest or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this paper to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we