arXiv subjects

Wenming Chen

Publications and source records attributed to Wenming Chen.

4 recordsLinked to original sources

arXiv2024

Broadband Dark Matter Axion Detection using a Cylindrical Capacitor

Cosmological axions/axion-like particles can compose a significant part of dark matter; however, the uncertainty of their mass is large. Here, we propose to search the axions using a cylindrical capacitor, in which the static electric field converts dark matter axions into an oscillating magnetic field. Due to the odd CPs, the axions couple to the electric field differently compared to the magnetic field. The axion couples to the electric field via a derivative that carries spatial information of incoming dark matter flux, while the coupling to the magnetic field depends on the dark matter density. This difference could be helpful in searching the axions and studies of the integrity of the theory, especially when the axions are very light, in which case the magnetic field-induced signal is DC-like. Orientation dependence could also be used to reduce the kinetic fluctuation-induced noise when multiple detectors operate simultaneously. In addition, a cylindrical setup shields the electric field to the laboratory and encompasses the axion-induced magnetic field within the capacitor. The induced oscillating magnetic field can then be picked up by a sensitive magnetometer. Adding a superconductor ring-coil system into the scheme can further boost the sensitivity and maintain the axion dark matter inherent bandwidth. This proposed setup could be capable of wide mass range searches.

hep-ph↗

arXiv2022

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

cs.CV↗

arXiv2013

A general variable neighborhood search for single-machine total tardiness scheduling problem with step-deteriorating jobs

In this article, we study a single-machine scheduling problem of minimizing the total tardiness for a set of independent jobs. The processing time of a job is modeled as a step function of its starting time and a specific deteriorating date. A mixed integer programming model was applied to the problem and validated. Since the problem is known to be NP-hard, we proposed a heuristic named simple weighted search procedure (SWSP) and a general variable neighborhood search algorithm (GVNS). A perturbation procedure with 3-opt is embedded within the GVNS process in order to explore broader spaces. Extensive numerical experiments are carried out on some randomly generated test instances so as to investigate the performance of the proposed algorithms. By comparing to the results of the CPLEX optimization solver, the heuristic SWSP and the standard variable neighborhood search, it is shown that the proposed GVNS algorithm can provide better solutions within a reasonable running time.

math.OC↗

arXiv2013

A hybrid generalized extremal optimization algorithm for the quay crane scheduling problem with interference constraints

The quay crane scheduling problem (QCSP) determines the handling sequence of tasks at ship bays by a set of cranes assigned to a container vessel such that the vessel's service time is minimized. A number of heuristics or meta-heuristics have been proposed to obtain the near-optimal solutions to overcome the NP-hardness of the problem. In this article, the idea of generalized extremal optimization (GEO) is adapted to solve the QCSP with respect to various interference constraints. The resulted GEO is termed as the modified GEO. A randomized searching method for neighboring task-to-QC assignments to an incumbent task-to-QC assignment is developed in executing the modified GEO. In addition, a unidirectional search decoding scheme is employed to transform a task-to-QC assignment to an active quay crane schedule. The effectiveness of the developed GEO is tested on a suite of benchmark problems introduced by \citet{KimPark2004}. Compared with other well known existing approaches, the experiment results show that the proposed modified GEO is capable of obtaining the optimal or near-optimal solution in reasonable time, especially for large-sized problems.

math.OC↗