Calibration of an interferometric surface measurement system on an ultra-precision turning lathe

On-machine measurement avoids the time-consuming transposition operations between the measurement and machine coordinates. The present work integrates an interferometric probing system on an ultra-precision turning machine. Due to the relatively harsh environment in the machine tools, metrological characteristics of the surface measurement instrument would deviate from those tested under standard laboratory conditions. In order to improve the performance of on-machine measurement systems, it is necessary to calibrate the on-machine measurement (OMM) system and compensate for any systematic errors. Three key issues, including on-machine vibration, machine tool kinematics error, and linearity error are discussed in this study. Experimental investigation is conducted to prove the validity of proposed calibration methodology and the effectiveness of on-machine measurement

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).Comment: accepted by CVPR202

MusicAOG: an Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music

In addressing the challenge of interpretability and generalizability of artificial music intelligence, this paper introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and granularities. Utilizing a hierarchical and-or graph representation, the model employs nodes and edges to encapsulate a broad spectrum of musical elements, including structures, textures, rhythms, and harmonies. This hierarchical approach expands the representability across various scales of music. This representation serves as the foundation for an energy-based model, uniquely tailored to learn musical concepts through a flexible algorithm framework relying on the minimax entropy principle. Utilizing an adapted Metropolis-Hastings sampling technique, the model enables fine-grained control over music generation. A comprehensive empirical evaluation, contrasting this novel approach with existing methodologies, manifests considerable advancements in interpretability and controllability. This study marks a substantial contribution to the fields of music analysis, composition, and computational musicology

Heating of multi‐species upflowing ion beams observed by Cluster on March 28, 2001

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149495/1/epp320083.pd

Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)

Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection

A ternary PEDOT-TiO2-reduced graphene oxide nanocomposite for supercapacitor applications

A ternary composite of PEDOT was prepared with TiO2 via emulsion polymerization method adjusting various weight ratios of TiO2 to PEDOT and synthesized rGO was then blended with this composite. The FTIR, UV–Vis and XRD analysis displayed characteristic features of PEDOT and TiO2. The morphology of the nano-hybrid structure was additionally investigated by SEM analysis. Pore size and surface area analysis of particles were characterized by BET method. The electrochemical analysis showed that the specific capacitance (Csp) for PEDOT-TiO2-15-rGO was 18.9 F.cm-2 at 0.1 mA g-1 current density