PAF-Net: Phase-Aligned Frequency Decoupling Network for Multi-Process Manufacturing Quality Prediction

• URL: http://arxiv.org/abs/2507.22840v1
• Date: Wed, 30 Jul 2025 16:56:42 GMT
• Title: PAF-Net: Phase-Aligned Frequency Decoupling Network for Multi-Process Manufacturing Quality Prediction
• Authors: Yang Luo, Haoyang Luan, Haoyun Pan, Yongquan Jia, Xiaofeng Gao, Guihai Chen,
• Abstract summary: We propose PAF-Net, a frequency decoupled time series prediction framework.<n>Phase-correlation alignment method guided by frequency domain energy to synchronize time-lagged quality series.<n>A frequency independent patch attention mechanism paired with Discrete Cosine Transform (DCT) decomposition to capture heterogeneous operational features.<n>Experiments on 4 real-world datasets demonstrate PAF-Net's superiority.
• Score: 29.64140868907608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate quality prediction in multi-process manufacturing is critical for industrial efficiency but hindered by three core challenges: time-lagged process interactions, overlapping operations with mixed periodicity, and inter-process dependencies in shared frequency bands. To address these, we propose PAF-Net, a frequency decoupled time series prediction framework with three key innovations: (1) A phase-correlation alignment method guided by frequency domain energy to synchronize time-lagged quality series, resolving temporal misalignment. (2) A frequency independent patch attention mechanism paired with Discrete Cosine Transform (DCT) decomposition to capture heterogeneous operational features within individual series. (3) A frequency decoupled cross attention module that suppresses noise from irrelevant frequencies, focusing exclusively on meaningful dependencies within shared bands. Experiments on 4 real-world datasets demonstrate PAF-Net's superiority. It outperforms 10 well-acknowledged baselines by 7.06% lower MSE and 3.88% lower MAE. Our code is available at https://github.com/StevenLuan904/PAF-Net-Official.

Related papers

• SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration [3.2735437407166414]
  Current dark image restoration methods suffer from severe efficiency bottlenecks.<n>We propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet)
  arXiv  Detail & Related papers  (2025-08-06T03:06:29Z)
• FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution [70.61549422952193]
  Face super-resolution (FSR) under limited computational costs remains an open problem.<n>Existing approaches typically treat all facial pixels equally, resulting in suboptimal allocation of computational resources.<n>We propose FADPNet, a Frequency-Aware Dual-Path Network that decomposes facial features into low- and high-frequency components.
  arXiv  Detail & Related papers  (2025-06-17T02:33:42Z)
• F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation [10.67983913373955]
  F2Net is a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized processing.<n>A Hybrid-Frequency Fusion module integrates these observations, guided by two novel objectives.<n>F2Net achieves state-of-the-art performance with mIoU of 80.22 and 83.39, respectively.
  arXiv  Detail & Related papers  (2025-06-09T15:09:49Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Efficient Time Series Forecasting via Hyper-Complex Models and Frequency Aggregation [1.024113475677323]
  Time series forecasting is a long-standing problem in statistics and machine learning.<n>We propose the Frequency Information Aggregation (FIA)-Net, which is based on a novel complex-valued architecture.<n>We evaluate the FIA-Net on various time-series benchmarks and show that the proposed methodologies outperform existing state of the art methods in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-02-27T11:03:37Z)
• ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting [12.604476544895762]
  This work introduces a novel Adaptive Mid-Frequency Energy, based on convolution and residual learning, to emphasize the significance of mid-frequency bands.<n>A novel Key-Frequency Enhanced Training strategy is employed to further enhance Key-Frequency modeling, where spectral information from other channels is randomly introduced into each channel.<n>Our approach advanced multivariate time series forecasting on the challenging Traffic, ECL, and Solar benchmarks, reducing MSE by 4%, 6%, and 5% compared to the previous SOTA iTransformer.
  arXiv  Detail & Related papers  (2025-02-24T06:40:33Z)
• FlowTS: Time Series Generation via Rectified Flow [67.41208519939626]
  FlowTS is an ODE-based model that leverages rectified flow with straight-line transport in probability space.<n>For unconditional setting, FlowTS achieves state-of-the-art performance, with context FID scores of 0.019 and 0.011 on Stock and ETTh datasets.<n>For conditional setting, we have achieved superior performance in solar forecasting.
  arXiv  Detail & Related papers  (2024-11-12T03:03:23Z)
• CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching [24.927390742543707]
  We introduce CATCH, a framework based on frequency patching.<n>We propose a Channel Fusion Module (CFM), which features a patch-wise mask generator and a masked-attention mechanism.<n>Experiments on 10 real-world datasets and 12 synthetic datasets demonstrate that CATCH achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-10-16T05:58:55Z)
• Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [67.60443290781988]
  Time series forecasting has played the key role in different industrial domains, including finance, traffic, energy, and healthcare. Most-based forecasting methods suffer from the point-wise mappings and information bottleneck. We propose FreTS, a simple yet effective architecture built upon Frequency-domains for Time Series forecasting.
  arXiv  Detail & Related papers  (2023-11-10T17:05:13Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.