Author name cluster

Le Zhou

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

EAAI Journal 2025 Journal Article

Boosting industrial anomaly detection performance using generated artificial fault data

Kai Wang
Jiayi Zhang
Yishun Liu
Jie Han
Xiaofeng Yuan
Le Zhou

Data-driven anomaly detection aims to learn a decision boundary, enveloping the normal region, and separating normal data from abnormal data. However, industrial data are fairly complex due to varying feedstock and unclear transfer processes and chemical reactions. This means the decision boundary will be very complex and even intractable. In addition, process variables are high-dimensional in modern industrial processes, which strengthens the difficulty of boundary extraction. Generally, the boundary should exactly exceed the outermost samples for precisely drawing normal regions. However, what we have in most situations is just normal data contaminated by unknown noises. Hence, conventional solutions that use statistical analysis to define a normal region result in a not-so-accurate decision boundary where missing alarms occur frequently. In addition to the conventional solution based entirely on historical data, i. e. , passive fault detection (PAD), an alternative detection method, active fault detection (AAD), can circumvent the above problem by stimulating system performance through the intervention of auxiliary signal. While it results in disruption of the normal operation conditions for the process, its method to enhance output performance through additional signals inspires us. In this paper, we resort to the ability of deep neural networks to fit nonlinear data and perform dimension reduction. A fault data generation strategy is proposed and the artificially generated fault data are used to regulate the model training. The new virtual fault data aids in suppressing the decision boundary closest to the outermost periphery. We propose the principles of data generation and form a network structure, implementing information fusion of genuine normal samples and virtual fault samples. Two cases demonstrate the efficiency of the proposed method.

Details DOI

ICML Conference 2025 Conference Paper

ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation

Tianci Bu
Le Zhou
Wenchuan Yang
Jianhong Mou
Kang Yang
Suoyi Tan
Feng Yao
Jingyuan Wang

Trajectory data is crucial for various applications but often suffers from incompleteness due to device limitations and diverse collection scenarios. Existing imputation methods rely on sparse trajectory or travel information, such as velocity, to infer missing points. However, these approaches assume that sparse trajectories retain essential behavioral patterns, which place significant demands on data acquisition and overlook the potential of large-scale human trajectory embeddings. To address this, we propose ProDiff, a trajectory imputation framework that uses only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and a denoising diffusion probabilistic model for robust spatiotemporal reconstruction. Joint training with a tailored loss function ensures effective imputation. ProDiff outperforms state-of-the-art methods, improving accuracy by 6. 28% on FourSquare and 2. 52% on WuXi. Further analysis shows a 0. 927 correlation between generated and real trajectories, demonstrating the effectiveness of our approach.

Details

JBHI Journal 2024 Journal Article

A Review of Depth-Based Human Motion Enhancement: Past and Present

Le Zhou
Nate Lannan
Guoliang Fan

In this article, we survey the current research trends of enhancement and denoising of depth-based motion capture data (D-Mocap) and also discuss possible future research issues. We first present the commonly used problem formulation for human motion enhancement. We then review related work and cover a broad set of methodologies including filtering based, learning based, and evolutionary based approaches. In addition, we present some important experiments-related issues, such as data creation or collection, reference data generation, and the metrics used for performance evaluation. It is our intent to provide a comprehensive tutorial and survey on the recent efforts on D-Mocap improvement, both methodologically and experimentally. By comparing the state-of-the-art methods, we also propose future research needs that could make D-Mocap more useful and relevant for real-world clinical applications.

Details DOI