Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection

Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain quality and facilitate grain circulation and processing. However, GAI is routinely performed manually by inspectors with cumbersome procedures, which poses a significant bottleneck in smart agriculture. In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp. By analyzing the distinctive characteristics of grain kernels, we formulate GAI as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible kernels are considered normal samples while damaged grains or unknown objects are regarded as anomalies. We further propose an AD model, called AD-GAI, which is trained using only normal samples yet can identify anomalies during inference. Moreover, we customize a prototype device for data acquisition and create a large-scale dataset including 220K high-quality images of wheat and maize kernels. Through extensive experiments, AD-GAI achieves considerable performance in comparison with advanced AD methods, and AI4GrainInsp has highly consistent performance compared to human experts and excels at inspection efficiency over 20x speedup. The dataset, code and models will be released at https://github.com/hellodfan/AI4GrainInsp.Comment: Accepted by ECAI2023. https://github.com/hellodfan/AI4GrainIns

Complex and diverse rupture processes of the 2018 Mw 8.2 and Mw 7.9 Tonga-Fiji deep earthquakes

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46(5), (2019):2434-2448, doi:10.1029/2018GL080997.Deep earthquakes exhibit strong variabilities in their rupture and aftershock characteristics, yet their physical failure mechanisms remain elusive. The 2018 Mw 8.2 and Mw 7.9 Tonga‐Fiji deep earthquakes, the two largest ever recorded in this subduction zone, occurred within days of each other. We investigate these events by performing waveform analysis, teleseismic P wave backprojection, and aftershock relocation. Our results show that the Mw 8.2 earthquake ruptured fast (4.1 km/s) and excited frequency‐dependent seismic radiation, whereas the Mw 7.9 earthquake ruptured slowly (2.5 km/s). Both events lasted ∼35 s. The Mw 8.2 earthquake initiated in the highly seismogenic, cold core of the slab and likely ruptured into the surrounding warmer materials, whereas the Mw 7.9 earthquake likely ruptured through a dissipative process in a previously aseismic region. The contrasts in earthquake kinematics and aftershock productivity argue for a combination of at least two primary mechanisms enabling rupture in the region.We thank the Editor Gavin Hayes and two anonymous reviewers for their helpful comments that improved the quality of the manuscript. The seismic data were provided by Data Management Center (DMC) of the Incorporated Research Institutions for Seismology (IRIS). The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms, related metadata, and/or derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Proposal of the National Science Foundation under Cooperative Agreement EAR‐1261681. W. F. acknowledges supports from the Postdoctoral Scholar Program at the Woods Hole Oceanographic Institution, with funding provided by the Weston Howland Postdoctoral Scholarship. S. S. W. and D. T. are supported by the MSU Geological Sciences Endowment.2019-08-2

Spectroscopic Confirmation of two Extremely Massive Protoclusters BOSS1244 and BOSS1542 at z=2.24z=2.24

We present spectroscopic confirmation of two new massive galaxy protoclusters at $z=2.24\pm0.02$, BOSS1244 and BOSS1542, traced by groups of Coherently Strong Ly$\alpha$ Absorption (CoSLA) systems imprinted in the absorption spectra of a number of quasars from the SDSS III and identified as overdensities of narrowband-selected H$\alpha$ emitters (HAEs). Using MMT/MMIRS and LBT/LUCI near-infrared (NIR) spectroscopy, we confirm 46 and 36 HAEs in the BOSS1244 and BOSS1542 fields, respectively. BOSS1244 displays a South-West (SW) component at $z=2.230\pm0.002$ and another North-East (NE) component at $z=2.246\pm0.001$ with the line-of-sight velocity dispersions of $405\pm202$ km s$^{-1}$ and $377\pm99$ km s$^{-1}$, respectively. Interestingly, we find that the SW region of BOSS1244 contains two substructures in redshift space, likely merging to form a larger system. In contrast, BOSS1542 exhibits an extended filamentary structure with a low velocity dispersion of $247\pm32$ km s$^{-1}$ at $z=2.241\pm0.001$, providing a direct confirmation of a large-scale cosmic web in the early Universe. The galaxy overdensities $\delta_{\rm g}$ on the scale of 15 cMpc are $22.9\pm4.9$, $10.9\pm2.5$, and $20.5\pm3.9$ for the BOSS1244 SW, BOSS1244 NE, and BOSS1542 filament, respectively. They are the most overdense galaxy protoclusters ($\delta_{\rm g}>20$) discovered to date at $z>2$. These systems are expected to become virialized at $z\sim0$ with a total mass of $M_{\rm SW}=(1.59\pm0.20)\times10^{15}$ $M_{\odot}$, $M_{\rm NE} =(0.83\pm0.11)\times10^{15}$ $M_{\odot}$ and $M_{\rm filament}=(1.42\pm0.18)\times10^{15}$ $M_{\odot}$, respectively. Together with BOSS1441 described in Cai et al. (2017a), these extremely massive overdensities at $z=2-3$ exhibit different morphologies, indicating that they are in different assembly stages in the formation of early galaxy clusters.Comment: 28 pages, 13 figures, 6 tables, accepted for publication in ApJ. The complete Abstract is presented in the manuscrip

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT

IntroductionThree-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.Materials and MethodsA total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.ResultsThe average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.ConclusionThe proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs

Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research

The response model of podded propulsion unmanned surface vehicle (USV) is established and identified; then considering the USV has characteristic of high speed, the course controller with fast convergence speed is proposed. The idea of MMG separate modeling is used to establish three-DOF planar motion model of the podded propulsion USV, and then the model is simplified as a response model. Then based on field experiments, the parameters of the response model are obtained by the method of system identification. Unlike ordinary ships, USV has the advantages of fast speed and small size, so the controller needs fast convergence speed and strong robustness. Based on the theory of multimode control, a fast nonsingular terminal sliding mode (FNTSM) course controller is proposed. In order to reduce the chattering of system, disturbance observer is used to compensate the disturbance to reduce the control gain and RBF neural network is applied to approximate the symbolic function. At the same time, fuzzy algorithm is employed to realize the mode soft switching, which avoids the unnecessary chattering when the mode is switched. Finally the rapidity and robustness of the proposed control approach are demonstrated by simulations and comparison studies