arXiv subjects

Ding Ning

Publications and source records attributed to Ding Ning.

12 recordsLinked to original sources

arXiv2026

AI Visual Inspection for Garment Production

The garment manufacturing industry is under increasing pressure to improve product quality, reduce costs, and accelerate digital transformation toward Industry 4.0. One of the most challenging quality-control activities is sewing-line inspection, where defects such as broken stitches and skipped stitches are difficult to detect consistently through manual inspection. Human-based inspection is often affected by fatigue, subjective judgement, and inconsistent performance, resulting in defect leakage, rework, and reduced production efficiency. This study presents the development and validation of an Artificial Intelligence (AI)-based visual inspection system for garment sewing-line quality control. The system utilizes Convolutional Neural Networks (CNNs) to detect sewing defects and was initially trained using black fabric and black sewing thread samples. Experimental testing was conducted on black, red, dark green, light blue, silver, and fluorescent yellow fabrics. The results demonstrated successful detection of jump sewing-line defects on black, red, and dark green materials, while performance limitations were observed for broken sewing-line defects and fabrics with significantly different visual characteristics, including light blue, silver, and fluorescent yellow colours. These findings indicate that model accuracy is strongly influenced by the diversity of training data and the ability to generalize across different fabric and thread colours.

cs.CV↗

arXiv2026

Coupling Chirality, Polar Order, and Altermagnetic Spin Splitting in a Hybrid Manganese Chloride

Hybrid manganese halides enable the coexistence of molecular chirality, polar order, and magnetic exchange within a single lattice. Here, we combine first-principles calculations with spin-space-group analysis to investigate the synthesized enantiomeric pair [(R)/(S)-MPA]2[MnCl4(H2O)] (MPA = beta-methylphenethylammonium). We predict that its compensated magnetic state hosts altermagnetic spin splitting in the nonrelativistic limit, and that the coupled chiral, polar, and magnetic degrees of freedom define a symmetry-related manifold. From this manifold, we derive simple sign rules for the electronic and magneto-optical response: reversing both chirality and polarity, or reversing the magnetic domain alone, inverts the spin splitting throughout the Brillouin zone, whereas reversing chirality alone or polarity alone changes the spin-splitting sign only in symmetry-selected regions. With spin-orbit coupling, reversing chirality or magnetic order flips the Kerr rotation angle, while changing the polar variant leaves it unchanged. These results reveal a chemically accessible route to translate molecular handedness into symmetry-controlled spin splitting and magneto-optical readout in hybrid manganese halides. Critically, we show that the sign and momentum pattern of the splitting are governed by the interplay of the chiral, polar, and magnetic degrees of freedom. This interplay opens the possibility to control the spin splitting through a judicious design of the organic cations, by modulating their chirality and polarity.

cond-mat.mtrl-sci↗

arXiv2025

Unconventional Magnetism, Sliding Ferroelectricity, and Magneto-Optical Kerr Effects in a Multiferroic Bilayer

Antiferromagnetic (AFM) materials offer a promising platform for exploring novel couplings between altermagnetic (AM) spin-splitting and magneto-optical Kerr effect (MOKE), with potential applications in next-generation quantum technologies. In this work, first-principles calculations, symmetry analysis, and kp modeling are employed to demonstrate how interlayer sliding in AFM multiferroic bilayers enables engineering of the electronic, magnetic, and magneto-optical properties. This study reveals an unprecedented dimension-driven AM crossover, where the 2D paraelectric (PE) bilayer exhibits spin-degenerate bands protected by the [C2||Mc] spin-space symmetry, while the 3D counterpart manifests AM spin-splitting along kz not equal to 0 paths. Furthermore, interlayer sliding breaks the Mc symmetry and stabilizes a ferroelectric (FE) state characterized by compensated ferrimagnetism and a Zeeman effect, which produces non-relativistic spin-split bands. In the FE phase, the inclusion of spin-orbit coupling (SOC) lifts accidental degeneracies, creating `alternating' spin-polarized bands due to the interplay of Zeeman and Rashba effects. Crucially, the spin polarization, ferro-valley polarization, and Kerr angle are simultaneously reversible by switching either interlayer sliding or the Neel vector. These findings highlight the rich coupling between electronic, magnetic, and optical orders in sliding multiferroics, thereby paving the way for ultra-low-power spintronics and optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2025

An Innovative Line Balancing for the Aluminium Melting Process

This research article explores the optimization of aluminium extrusion processes through advanced line balancing techniques, focusing on maximizing marginal profit by increasing melting and casting outputs. By employing mixed integer linear programming (MILP), we identify strategies to minimize idle costs and enhance production efficiency. The study demonstrates that increasing the daily cycle rate from 2 to 4.36 cycles results in a significant rise in daily marginal profit, calculated at USD67,786, after accounting for additional labour costs. This optimization is achieved by expanding the workforce from 8 to 12 operators across two shifts, leading to a 50% increase in labour expenses. The findings reveal a remarkable 117.6% growth in marginal daily profit, underscoring the potential of automation and intelligent manufacturing in transforming the aluminium extrusion industry. Insights from cross-industry research, including Lean Methodology in the Modern Garment Industry, further illustrate the broader applicability of these advancements. This study highlights the critical role of automation in driving productivity and profitability in manufacturing sectors, paving the way for future innovations in aluminium extrusion and beyond.

math.OC↗

arXiv2025

A Mixed-Integer Linear Programming (MILP) for Garment Line Balancing

This applied research article explores the application of Mixed-Integer Linear Programming (MILP) to address line-balancing challenges in the garment industry, focusing on optimizing production processes under multiple constraints. By integrating MILP with Lean Methodology principles, the study demonstrates significant improvements in operational efficiency and cost-effectiveness. The case study, conducted in collaboration with Prof Dr Ray WM Kong, highlights the successful implementation of MILP using IBM CPLEX Studio to optimize production order quantities across online and offline operations. The results reveal a remarkable reduction in labour costs, exceeding 50%, while effectively managing resource capacity and demand constraints. This study not only validates the theoretical underpinnings of MILP in resolving line-balancing issues but also underscores its practical applicability in modernizing garment production. The findings contribute valuable insights into the potential of advanced optimization techniques to enhance competitiveness and sustainability in the garment industry. This abstract succinctly captures the essence of the research, emphasizing the methodology, results, and significance of the study.

math.OC↗

arXiv2025

A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models

Marine heatwaves (MHWs) are extreme ocean-temperature events with significant impacts on marine ecosystems and related industries. Accurate forecasts (one to six months ahead) of MHWs would aid in mitigating these impacts. However, forecasting MHWs presents a challenging imbalanced regression task due to the rarity of extreme temperature anomalies in comparison to more frequent moderate conditions. In this study, we examine monthly MHW forecasts for 12 locations around New Zealand. We use a fully-connected neural network and compare standard and specialized regression loss functions, including the mean squared error (MSE), the mean absolute error (MAE), the Huber, the weighted MSE, the focal-R, the balanced MSE, and a proposed scaling-weighted MSE. Results show that (i) short lead times (one month) are considerably more predictable than three- and six-month leads, (ii) models trained with the standard MSE or MAE losses excel at forecasting average conditions but struggle to capture extremes, and (iii) specialized loss functions such as the balanced MSE and our scaling-weighted MSE substantially improve forecasting of MHW and suspected MHW events. These findings underscore the importance of tailored loss functions for imbalanced regression, particularly in forecasting rare but impactful events such as MHWs.

physics.ao-ph↗

arXiv2025

Line Balancing in the Modern Garment Industry

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

cs.RO↗

arXiv2025

Diving Deep: Forecasting Sea Surface Temperatures and Anomalies

This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.

cs.LG↗

arXiv2024

Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach

Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale. The approach combines graph representation for modeling spatial properties in climate data, imbalanced regression to handle skewed data distributions, and temporal diffusion to enhance forecast accuracy across various lead times. To the best of our knowledge, this is the first study that synthesizes three spatiotemporal anomaly methodologies to predict MHWs. Additionally, we introduce a method for constructing graphs that avoids isolated nodes and provide a new publicly available sea surface temperature anomaly graph dataset. We examine the trade-offs in the selection of loss functions and evaluation metrics for MHWs. We analyze spatial patterns in global MHW predictability by focusing on historical hotspots, and our approach demonstrates better performance compared to traditional numerical models in regions such as the middle south Pacific, equatorial Atlantic near Africa, south Atlantic, and high-latitude Indian Ocean. We highlight the potential of temporal diffusion to replace the conventional sliding window approach for long-term forecasts, achieving improved prediction up to six months in advance. These insights not only establish benchmarks for machine learning applications in MHW forecasting but also enhance understanding of general climate forecasting methodologies.

physics.ao-ph↗

arXiv2023

Graph-Based Deep Learning for Sea Surface Temperature Forecasts

Sea surface temperature (SST) forecasts help with managing the marine ecosystem and the aquaculture impacted by anthropogenic climate change. Numerical dynamical models are resource intensive for SST forecasts; machine learning (ML) models could reduce high computational requirements and have been in the focus of the research community recently. ML models normally require a large amount of data for training. Environmental data are collected on regularly-spaced grids, so early work mainly used grid-based deep learning (DL) for prediction. However, both grid data and the corresponding DL approaches have inherent problems. As geometric DL has emerged, graphs as a more generalized data structure and graph neural networks (GNNs) have been introduced to the spatiotemporal domains. In this work, we preliminarily explored graph re-sampling and GNNs for global SST forecasts, and GNNs show better one month ahead SST prediction than the persistence model in most oceans in terms of root mean square errors.

physics.ao-ph↗

arXiv2021

Vision-based Navigation for a Small-scale Quadruped Robot Pegasus-Mini

Quadruped locomotion is currently a vibrant research area, which has reached a level of maturity and performance that enables some of the most advanced real-world applications with autonomous quadruped robots both in academia and industry. Blind robust quadruped locomotion has been pushed forward in control and technology aspects within recent decades. However, in the complicated environment, the capability including terrain perception and path planning is still required. Visual perception is an indispensable ability in legged locomotion for such a demand. This study explores a vision-based navigation method for a small-scale quadruped robot Pegasus-Mini, aiming to propose a method that enables efficient and reliable navigation for the small-scale quadruped locomotion. The vision-based navigation method proposed in this study is applicable in such a small-scale quadruped robot platform in which the computation resources and space are limited. The semantic segmentation based on a CNN model is adopted for the real-time path segmentation in the outdoor environment. The desired traverse trajectory is generated through real-time updating the middle line, which is calculated from the edge position of the segmented path in the images. To enhance the stability of the path planning directly based on the semantic segmentation method, a trajectory compensation method is supplemented considering the temporal information to revise the untrustworthy planned path. Experiments of semantic segmentation and navigation in a garden scene are demonstrated to verify the effectiveness of the proposed method.

cs.RO↗

arXiv2020

Magnetotransport Properties of Square-Net Compounds of NbSiSb and NbGeSb Single Crystals

We successfully grew single crystals of Si- and Ge-square-net compounds of NbSiSb and NbGeSb whose excellent crystalline quality are verified using single-crystal x-ray diffraction, rocking curves, scanning and transmission electron microscopies. Since these two compounds share major crystallographic similarity with the topological nodal-line semimetals of ZrSiS family, we employ density functional theory (DFT) calculations and magnetotransport measurements to demonstrate their band structures as well as the electron scattering mechanisms. DFT calculations show that the fermiology shows strong anisotropy from the crystallographic c-axis to the ab-plane and weak anisotropy within the ab plane, which is consistent with the strong anisotropic magnetotransport behaviors. Following the Kohler's scaling rule we prove that similar interband and intraband electron-phonon scattering mechanisms work in both the NbSiSb and NbGeSb compounds. The study of electronic transport mechanism in the presence of external magnetic field renders deep insight into topological behavior together with it's Fermi surface, and the high similarity of crystallography and strong difference in band structures between the present single crystals and that of ZrSiS family provides the possibility to tune the band structure via element doping

cond-mat.mtrl-sci↗