Probabilistic Models for Order-Picking Operations with Multiple in-the-Aisle Pick Positions

The development of probability density functions (pdfs) for travel time of a narrow aisle lift truck (NALT) and an automated storage and retrieval (AS/R) machine is the focus of the dissertation. The multiple in-the-aisle pick positions (MIAPP) order picking system can be modeled as an M/G/1 queueing problem in which storage and retrieval requests are the customers and the vehicle (NALT or AS/R machine) is the server. Service time is the sum of travel time and the deterministic time to pick up and deposit a pallet (TPD). Our first contribution is the development of travel time pdfs for retrieval operations in an MIAPP order picking system supported by a narrow aisle lift truck (MIAPP-NALT); storage operations are assumed to occur when order picking is not being performed. A rectilinear travel metric is used for the NALT; pdfs are derived and finite population queueing and infinite population queueing models are used to analyze the retrieval operations under stochastic conditions. Our second contribution is the development of travel time pdfs for retrieval operations in an MIAPP order picking system supported by an AS/R machine (MIAPP-AS/RS); storage operations are assumed to occur when order picking is not being performed. A Chebyshev travel metric is used for the AS/R machine. For the MIAPP-AS/RS operation, pick positions are located at floor level and on a mezzanine. The pdfs for four scenarios are derived and finite population and infinite population queueing models are used to analyze the retrieval operation under stochastic conditions. Our final contribution is the development of travel time pdfs for storage and retrieval operations in an MIAPP-NALT system with two classes of stock keeping units (skus): fast movers and slow movers. A rectilinear travel metric is used and two levels of pick positions are considered. Non-preemptive priority queueing and non-priority queueing models are used to analyze storage and retrieval requests in the MIAPP-NALT system. Retrieval requests are given a higher priority than storage requests; alternately, storage and retrieval requests are served using a first come, first serve (FCFS) discipline

A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration

Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion which may interfere with the stage of image registration and stitching. Aiming to resolve this drawback, we devise a stronger stitching algorithm for fisheye images by combining the traditional image processing method with deep learning. In the stage of fisheye image correction, we propose the Attention-based Nonlinear Activation Free Network (ANAFNet) to deblur fisheye images corrected by Zhang calibration method. Specifically, ANAFNet adopts the classical single-stage U-shaped architecture based on convolutional neural networks with soft-attention technique and it can restore a sharp image from a blurred image effectively. In the part of image registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image matching algorithm, to improve the accuracy of image registration. Experimental results demonstrate that panoramic images of superior quality stitching by fisheye images can be obtained through our method.Comment: 6 pages, 5 figure

The Segmentation of Wear Particles Images Using J

This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system

GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds

LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to the severe domain gap problem. Existing domain adaptive 3D detection methods do not adequately consider the problem of the distributional discrepancy in feature space, thereby hindering generalization of detectors across domains. In this work, we propose a novel unsupervised domain adaptive \textbf{3D} detection framework, namely \textbf{G}eometry-aware \textbf{P}rototype \textbf{A}lignment (\textbf{GPA-3D}), which explicitly leverages the intrinsic geometric relationship from point cloud objects to reduce the feature discrepancy, thus facilitating cross-domain transferring. Specifically, GPA-3D assigns a series of tailored and learnable prototypes to point cloud objects with distinct geometric structures. Each prototype aligns BEV (bird's-eye-view) features derived from corresponding point cloud objects on source and target domains, reducing the distributional discrepancy and achieving better adaptation. The evaluation results obtained on various benchmarks, including Waymo, nuScenes and KITTI, demonstrate the superiority of our GPA-3D over the state-of-the-art approaches for different adaptation scenarios. The MindSpore version code will be publicly available at \url{https://github.com/Liz66666/GPA3D}.Comment: Accepted by ICCV 202

Anesthetic and hemodynamic effects of etomidateremifentanil combination in laparoscopic surgery

Purpose: To study the anesthetic and hemodynamic effects of etomidate-remifentanil combination treatment in laparoscopic surgery.Methods: Patients scheduled for gynecological laparoscopic surgery (n = 120) were assigned to test and control groups (60 patients each). Etomidate combined with remifentanil anesthesia was used in the test group, while propofol-remifentanil combination anesthesia was used in the control group. The effect of anesthesia on awakening time, extubation time, pain relief time, systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) of patients before and after the extubation were observed and recorded for the two groups.Results: Excellent rating was 98.0 % in the test group, and was superior to the corresponding rating of 86.0 % in the control group. Anesthesia time, awakening time, extubation time and pain relief time were markedly shorter in the test group than in controls (p < 0.05). However, there were no significant differences in SBP, DBP and HR of patients with tracheal intubation between the two groups (p > 0.05). The results were similar in patients with laparoscopic placement. After laparoscopic placement and tracheal extubation, significant decreases in SBP and HR in the test group were seen, relative to control patients (p < 0.05).Conclusion: The anesthetic effect of etomidate combined with remifentanil is superior to that of propofol and remifentanil, and ensures stability of hemodynamic parameters such as SBP, DBP and HR during the period of anesthesia.Keywords: Etomidate, Remifentanil, Propofol, Laparoscopic surgery, Hemodynamic parameter