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Ji Zhao

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7 papers

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EAAI Journal 2025 Journal Article

Knowledge and data dual-driven cyber-physics system for intelligent monitoring and compensation of machine tool dynamic coupling error

Chengyi Wu
Shijun Ji
Ji Zhao

The interpretability of data-driven cyber-physics system (CPS) is limited, while traditional model-driven methods struggle with the time-varying requirements of dynamic performance. CPS combined with the artificial intelligence (AI) is used to monitor and compensate the states of machine tools, which is becoming increasingly important for machining under dynamic working conditions. This paper proposes a knowledge and data dual-driven cyber-physics system to control the dynamic coupling error of the machine tool and improve the contour accuracy. The proposed hybrid deep learning model is combined with the temporal attention mechanism to extract dynamic information of important features from historical data. Theoretical models are constructed to study the coupling effect among thrust harmonics, non-uniform rational B-Splines interpolation errors and kinematic constraints, and they are embedded as the prediction boundary of the hybrid deep learning to improve the interpretability of the model. The data interaction between the cyber module and physical module creates a closed loop to realize the deep integration. Multiple case studies have verified the feasibility of convolutional neural network and gate recurrent unit in terms of compensation accuracy and their robustness under complex working conditions. The minimum root mean square error predicted on the self-constructed dataset is 7. 3649 × 10−5 mm. For machining under different position-velocity-time interpolation periods, the minimum average contour error is 5. 6424 × 10−4 mm, which is 44. 19 % lower than other traditional methods. This study proposes a unified framework for the deep integration of CPS and AI algorithms, providing significant insights into the contour accuracy compensation of ultra-precision machining.

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EAAI Journal 2025 Journal Article

Research, application, and challenges of causal inference in industrial fault diagnosis: A survey

Bo Li
Qiang Li
Tingfeng Du
Dong Liu
Qiang Yang
Tianxiang Chen
Jing Xiong
Bo Peng

Industrial fault diagnosis technologies leveraging convolutional neural networks and other advanced neural network architectures are pivotal for ensuring stable equipment operation, enhancing production efficiency, and minimizing maintenance costs. Nevertheless, these methods encounter inherent challenges due to data constraints and the complexity of production environments, particularly in identifying fault root causes and ensuring the interpretability of models. The integration of causal inference into industrial fault diagnosis offers significant promise for elucidating fault propagation pathways, revealing causal interrelations within complex systems, and advancing model interpretability. This survey presents a holistic review of research trajectories, pivotal technologies, and methodological advancements in causal inference for industrial fault diagnosis while systematically delineating the advantages and prospective challenges in this domain. First, this paper examines the limitations of conventional machine-learning approaches in fault diagnosis and traces the evolutionary trajectory of causal inference development in this context. Subsequently, the core theories and foundational technologies underpinning causal inference in industrial fault diagnosis are comprehensively discussed. Following this, the survey categorizes the existing literature according to different causal inferences to solve specific problems in industrial fault diagnosis and delves into detailed case studies, underscoring their utility in addressing distinct challenges. Finally, this survey synthesizes insights from existing literature to encapsulate the merits of causal inference in industrial fault diagnosis and to elucidate the prospective challenges it may encounter.

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IJCAI Conference 2024 Conference Paper

Dialogue Cross-Enhanced Central Engagement Attention Model for Real-Time Engagement Estimation

Jun Yu
Keda Lu
Ji Zhao
Zhihong Wei
Iek-Heng Chu
Peng Chang

Real-time engagement estimation has been an important research topic in human-computer interaction in recent years. The emergence of the NOvice eXpert Interaction (NOXI) dataset, enriched with frame-wise engagement annotations, has catalyzed a surge in research efforts in this domain. Existing feature sequence partitioning methods for ultra-long videos have encountered challenges including insufficient information utilization and repetitive inference. Moreover, those studies focus mainly on the target participants’ features without taking into account those of the interlocutor. To address these issues, we propose the center-based sliding window method to obtain feature subsequences. The core of these subsequences is modeled using our innovative Central Engagement Attention Model (CEAM). Additionally, we introduce the dialogue cross-enhanced module that effectively incorporates the interlocutor’s features via cross-attention. Our proposed method outperforms the current best model, achieving a substantial gain of 1. 5% in coordination correlation coefficient (CCC) and establishing a new state-of-the-art result. Our source codes and model checkpoints are available at https: //github. com/wujiekd/Dialogue-Cross-Enhanced-CEAM.

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AAAI Conference 2019 Conference Paper

Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning

Ji Zhao
Dan Peng
Chuhan Wu
Huan Chen
Meiyu Yu
Wanji Zheng
Li Ma
Hua Chai

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

Locality Preserving Matching

Jiayi Ma
Ji Zhao
Hanqi Guo
Junjun Jiang
Huabing Zhou
Yuan Gao

Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. More specifically, our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. Experiments on various real image pairs for general feature matching, as well as for visual homing and image retrieval demonstrate the generality of our method for handling different types of image deformations, and it is more than two orders of magnitude faster than state-of-the-art methods in the same range of or better accuracy.

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AAAI Conference 2017 Conference Paper

Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization

Jiayi Ma
Ji Zhao
Junjun Jiang
Huabing Zhou

In this paper, we propose a robust transformation estimation method based on manifold regularization for non-rigid point set registration. The method iteratively recovers the point correspondence and estimates the spatial transformation between two point sets. The correspondence is established based on existing local feature descriptors which typically results in a number of outliers. To achieve an accurate estimate of the transformation from such putative point correspondence, we formulate the registration problem by a mixture model with a set of latent variables introduced to identify outliers, and a prior involving manifold regularization is imposed on the transformation to capture the underlying intrinsic geometry of the input data. The non-rigid transformation is speciﬁed in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on both 2D and 3D data demonstrate that our method can yield superior results compared to other state-ofthe-arts, especially in case of badly degraded data.

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AAAI Conference 2013 Conference Paper

A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries

Deyu Meng
Zongben Xu
Lei Zhang
Ji Zhao

A challenging problem in machine learning, information retrieval and computer vision research is how to recover a low-rank representation of the given data in the presence of outliers and missing entries. The L1-norm low-rank matrix factorization (LRMF) has been a popular approach to solving this problem. However, L1-norm LRMF is difficult to achieve due to its non-convexity and non-smoothness, and existing methods are often inefficient and fail to converge to a desired solution. In this paper we propose a novel cyclic weighted median (CWM) method, which is intrinsically a coordinate decent algorithm, for L1-norm LRMF. The CWM method minimizes the objective by solving a sequence of scalar minimization sub-problems, each of which is convex and can be easily solved by the weighted median filter. The extensive experimental results validate that the CWM method outperforms state-of-the-arts in terms of both accuracy and computational efficiency.

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