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Peng Yu

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21 papers

2 author rows

AAAI Conference 2026 Conference Paper

Binary Split Categorical Feature with Mean Absolute Error Criteria in CART

Peng Yu
Yike Chen
Chao Xu
Albert Bifet
Jesse Read

In the context of the Classification and Regression Trees (CART) algorithm, the efficient splitting of categorical features using standard criteria like GINI and Entropy is well-established. However, using the Mean Absolute Error (MAE) criterion for categorical features has traditionally relied on various numerical encoding methods. This paper demonstrates that unsupervised numerical encoding methods are not viable for MAE criteria. Furthermore, we present a novel and efficient splitting algorithm that addresses the challenges of handling categorical features with the MAE criterion. Our findings underscore the limitations of existing approaches and offer a promising solution to enhance the handling of categorical data in CART algorithms.

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EAAI Journal 2026 Journal Article

Dynamic vision-based machinery intelligent fault diagnosis with robustness on camera positions

Xiang Li
Peng Yu
Bin Yang
Yaguo Lei
Naipeng Li
Ke Feng

The event cameras have been successfully developed and applied in different areas in the past years. With the significant advantages of high temporal resolution, low latency, etc. , many challenging tasks have been addressed, such as drone vision, fast object detection and so forth. The event cameras are promising for machine condition monitoring and fault diagnosis, since the vibration effects can be well captured by the bio-inspired vision technology. Using the well-established big data-driven methodologies, the intelligent vision-based fault diagnosis model can be readily built. However, the vision signal suffers from remarkable disturbances in positions, views, distances, etc. Variations in signal collection can easily compromise the model performance, which frequently occur in practice. In this paper, an dynamic vision-based machinery intelligent fault diagnosis method is proposed with special attention on camera position robustness. By creating the self-generated event sample variants, a self-supervised event learning method is proposed for domain generalization regarding camera positions. For processing the easily collected unlabeled parallel data at different camera positions, a cross-supervision learning method is proposed for domain alignment. Extensive experiments on the dynamic vision-based rotating machine fault diagnosis test rig are carried out for validations. The results show the proposed method can effectively extract domain-invariant features from the dynamic vision data, and identify the machine fault modes at different camera positions. It is validated that the proposed method offers a novel perspective and promising tool for robust machine fault diagnosis using vision data from event cameras.

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AAAI Conference 2025 Conference Paper

EchoDiffusion: Waveform Conditioned Diffusion Models for Echo-Based Depth Estimation

Wenjie Zhang
Jun Yin
Long Ma
Peng Yu
Xiaoheng Jiang
Zhen Tian
Mingliang Xu

To extract spatial information, depth estimation using conventional echo-based methods typically employs models with encoder-decoder architectures, such as UNet. However, these methods may face challenges in extracting fine details from echo waveforms and handling multi-scale feature extraction with high precision. To address these challenges, we introduce EchoDiffusion, a framework that incorporates diffusion models conditioned on waveform embeddings for echo-based depth estimation. This framework employs the Multi-Scale Adaptive Latent Feature Network (MALF-Net) to extract multi-scale spatial features and perform adaptive fusion, encoding the echo spectrograms into the latent space. Additionally, we propose the Echo Waveform Detail Embedder (EWDE), which leverages a pre-trained Wav2Vec model to extract detailed spatial information from echo waveforms, using these details as conditional inputs to guide the reverse diffusion process in the latent space. By embedding the echo waveforms into the reverse diffusion process, we can more accurately guide the generation of depth maps. Our extensive evaluations on the Replica and Matterport3D datasets demonstrate that EchoDiffusion establishes new benchmarks for state-of-the-art performance in echo-based depth estimation.

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IROS Conference 2025 Conference Paper

Iterative Learning Motion Control of Continuum Robots Based on Neural Ordinary Differential Equations

Zhenhan Liang
Peng Yu
Ning Tan

Traditional data-driven control methods often require large amounts of training data, posing significant challenges for continuum robots. Recently, neural ordinary differential equation (NODE) methods have demonstrated impressive capabilities for data-efficient modeling of continuum robots. However, existing NODE-based control methods still face limitations in terms of convergence and robustness. In this paper, we propose a data-driven iterative learning control system for continuum robots, leveraging NODE for modeling. Within this framework, by incorporating online parameter learning, the proposed control system continuously adapts to various uncertainties associated with continuum robots, resulting in improved convergence and robustness in repetitive tasks. The effectiveness of the proposed method is validated through simulations and physical experiments, and comparative analysis highlights its superior accuracy over existing approaches.

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EAAI Journal 2025 Journal Article

Multimodal interval prediction of carbon dioxide emissions from heavy construction machinery: a missing-data robust inverted-transformer model considering sensors failure in complex construction environment

Zhouquan Dong
Xiaoling Wang
Jun Zhang
Peng Yu
Zhijian Cai

Accurate forecasting of carbon dioxide (CO2) emissions from heavy construction machinery in large-scale infrastructure projects presents a viable avenue for mitigating climate change. However, most current studies neglect the intricate operating conditions of such machinery. Moreover, in complex construction environments with high-frequency vibrations and heavy dust, sensor failures leading to random data loss significantly increase the difficulty of emission prediction. Especially when high-emission periods account for a small proportion, existing methods struggle to effectively capture CO2 emission peaks. To address these challenges, this study proposes an improved Inverted-Transformer (iTransformer) multimodal interval prediction model for accurate CO2 emission forecasting in heavy construction machinery under conditions of random sensor failures. The model incorporates a Mixture of Experts (MoE) mechanism within the iTransformer framework, which adaptively adjusts expert weights for missing modalities, thereby reducing prediction errors caused by random data loss. Additionally, to capture localized CO2 emission peaks, this study introduces a Peak Capture Loss (PCL) function, which adjusts incremental emissions between adjacent time steps by supervising the differences between generated sequences, enabling the model to track abrupt emission variations. The Bootstrap method is also utilised to quantify and estimate uncertainty in the CO2 emission Interval Prediction. Case studies reveal that the proposed model achieves high prediction accuracy (coefficient of determination (R 2 ) = 0. 99), especially across various data missing rates (5 %, 10 %, 15 %), with the average R 2 value increasing by approximately 6 %. This provides a novel approach for predicting emissions of heavy construction machinery in large-scale infrastructure projects.

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EAAI Journal 2023 Journal Article

Comparative studies and performance analysis on neural-dynamics-driven control of redundant robot manipulators with unknown models

Peng Yu
Ning Tan
Zhiyan Zhong

This paper proposes an inverse-free and model-free control scheme based on gradient neural dynamics (GND), which avoids the calculation of pseudo-inverse, to achieve the tracking control of redundant robot manipulators without knowing their kinematic models. Specifically, two GND models are deployed to solve the inverse kinematics problem and to estimate the unknown Jacobian matrix of manipulators respectively. We prove that the residual tracking error associated with the proposed scheme theoretically converges to an arbitrarily small upper bound in finite-time. Besides, combining GND and zeroing neural dynamics (ZND), this paper also proposes two control schemes based on hybrid neural dynamics to further achieve better performance. Moreover, the proposed continuous-time control schemes are improved to discrete-time algorithms to facilitate the deployment. Finally, the feasibility and merits of the proposed control schemes are revealed by experiments and comparisons.

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NeurIPS Conference 2022 Conference Paper

Linear tree shap

Peng Yu
Albert Bifet
Jesse Read
Chao Xu

Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.

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EAAI Journal 2021 Journal Article

Cluster-based fine-to-coarse superpixel segmentation

Xiangjun Li
Yong Zhou
Xinping Zhang
Su Xu
Peng Yu

As an image preprocessing technology, superpixel segmentation has become an important tool in the field of computer vision. How to obtain a more accurate, faster, and easier-to-apply superpixel segmentation algorithm is a problem faced by researchers. In this paper, a cluster-based fine-to-coarse superpixel segmentation (FCSS) algorithm is proposed. By introducing color thresholds and depth thresholds with practical physical meanings as algorithm parameters, high-quality segmentation with fewer superpixels is achieved. It not only reduces the complexity of the upper application, but also provides an easy to understand interface. Superpixel segmentation methods often cannot achieve high-quality segmentation through a set of parameters. Experimental results show that FCSS can achieve finer segmentation by setting different parameters, and the segmentation results are superior to other algorithms. When the number of superpixels is 100, the segmentation performance of FCSS is better than that of existing state-of-the-art methods.

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JAIR Journal 2017 Journal Article

Resolving Over-Constrained Temporal Problems with Uncertainty through Conflict-Directed Relaxation

Peng Yu
Brian Williams
Cheng Fang
Jing Cui
Patrik Haslum

Over-subscription, that is, being assigned too many things to do, is commonly encountered in temporal scheduling problems. As human beings, we often want to do more than we can actually do, and underestimate how long it takes to perform each task. Decision makers can benefit from aids that identify when these failure situations are likely, the root causes of these failures, and resolutions to these failures. In this paper, we present a decision assistant that helps users resolve over-subscribed temporal problems. The system works like an experienced advisor that can quickly identify the cause of failure underlying temporal problems and compute resolutions. The core of the decision assistant is the Best-first Conflict-Directed Relaxation (BCDR) algorithm, which can detect conflicting sets of constraints within temporal problems, and computes continuous relaxations for them that weaken constraints to the minimum extent, instead of removing them completely. BCDR is an extension to the Conflict-Directed A* algorithm, first developed in the model-based reasoning community to compute most likely system diagnoses or reconfigurations. It generalizes the discrete conflicts and relaxations, to hybrid conflicts and relaxations, which denote minimal inconsistencies and minimal relaxations to both discrete and continuous relaxable constraints. In addition, BCDR is capable of handling temporal uncertainty, expressed as either set-bounded or probabilistic durations, and can compute preferred trade-offs between the risk of violating a schedule requirement, versus the loss of utility by weakening those requirements. BCDR has been applied to several decision support applications in different domains, including deep-sea exploration, urban travel planning and transit system management. It has demonstrated its effectiveness in helping users resolve over-subscribed scheduling problems and evaluate the robustness of existing solutions. In our benchmark experiments, BCDR has also demonstrated its efficiency on solving large-scale scheduling problems in the aforementioned domains. Thanks to its conflict-driven approach for computing relaxations, BCDR achieves one to two orders of magnitude improvements on runtime performance when compared to state-of-the-art numerical solvers.

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IROS Conference 2016 Conference Paper

A design of phase-closed-loop nanomachining control based ultrasonic vibration-assisted AFM

Jialin Shi
Lianqing Liu
Peng Yu
Yang Cong

This paper proposed a phase-closed-loop nanomachining control method to realize the directly control of machining depth based on ultrasonic vibration-assisted AFM. By using applied force to control the machining depth, conventional AFM machining approaches unable to machining a nanostructure with specified machined depth. With the proposed method, the vibration phase of micro-cantilever has a specific relationship with machining depth. Therefore, the nano-grooves with desired depth can be machined by using phase value as feedback of PID control. In this paper, the theoretical analysis and simulation are carried out, and the experiments of phase-closed-loop control method are conducted. The experimental results verify the primary feasibility of the proposed method. The present method also demonstrates the potential on the fabrication of three-dimension nanostructures and nanoelectronic device.

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JAIR Journal 2016 Journal Article

Association Discovery and Diagnosis of Alzheimers Disease with Bayesian Multiview Learning

Zenglin Xu
Shandian Zhe
Yuan Qi
Peng Yu

The analysis and diagnosis of Alzheimers disease (AD) can be based on genetic variations, e.g., single nucleotide polymorphisms (SNPs) and phenotypic traits, e.g., Magnetic Resonance Imaging (MRI) features. We consider two important and related tasks: i) to select genetic and phenotypical markers for AD diagnosis and ii) to identify associations between genetic and phenotypical data. While previous studies treat these two tasks separately, they are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. Here we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels; in return, the disease status can guide the discovery of relationships between data sources. The sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease. Moreover, to take advantage of the linkage disequilibrium (LD) measuring the non-random association of alleles, we incorporate a graph Laplacian type of prior in the model. To learn the model from data, we develop an efficient variational inference algorithm. Analysis on an imaging genetics dataset for the study of Alzheimers Disease (AD) indicates that our model identifies biologically meaningful associations between genetic variations and MRI features, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

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IJCAI Conference 2016 Conference Paper

Resolving Over-Constrained Conditional Temporal Problems Using Semantically Similar Alternatives

Peng Yu
Jiaying Shen
Peter Z. Yeh
Brian Williams

In recent literature, several approaches have been developed to solve over-constrained travel planning problems, which are often framed as conditional temporal problems with discrete choices. These approaches are able to explain the causes of failure and recommend alternative solutions by suspending or weakening temporal constraints. While helpful, they may not be practical in many situations, as we often cannot compromise on time. In this paper, we present an approach for solving such over-constrained problems, by also relaxing non-temporal variable domains through the consideration of additional options that are semantically similar. Our solution, called Conflict-Directed Semantic Relaxation (CDSR), integrates a knowledge base and a semantic similarity calculator, and is able to simultaneously enumerate both temporal and domain relaxations in best-first order. When evaluated empirically on a range of urban trip planning scenarios, CDSR demonstrates a substantial improvement in flexibility compared to temporal relaxation only approaches.

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ICAPS Conference 2015 Conference Paper

Optimising Bounds in Simple Temporal Networks with Uncertainty under Dynamic Controllability Constraints

Jing Cui
Peng Yu
Cheng Fang
Patrik Haslum
Brian Williams 0001

Dynamically controllable simple temporal networks with uncertainty (STNU) are widely used to represent temporal plans or schedules with uncertainty and execution flexibility. While the problem of testing an STNU for dynamic controllability is well studied, many use cases — for example, problem relaxation or schedule robustness analysis — require optimising a function over STNU time bounds subject to the constraint that the network is dynamically controllable. We present a disjunctive linear constraint model of dynamic controllability, show how it can be used to formulate a range of applications, and compare a mixed-integer, a non-linear programming, and a conflict-directed search solver on the resulting optimisation problems. Our model also provides the first solution to the problem of optimisation over a probabilistic STN subject to dynamic controllability and chance constraints.

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IROS Conference 2015 Conference Paper

Real-time detecting and tracking nanoscale feeble vibrations based SF-AM AFM

Jialin Shi
Lianqing Liu
Peng Yu
Peng Li 0057

Nanoscale vibration, a critical nanomechanical property of cell membranes/walls, is a crucial aspect of cell physiology. However, limitations of current nanoscale vibration detecting methods remain the major obstacle for scientific study and cell vibration experiments. Due to the absence of effective method of feeble nanoscale vibration detecting, most sorts of quantitative and dynamic cell vibrations cannot be observed. Therefore, a real-time tracking detection method is vital for the study of cell physiology. In this paper, a real-time tracking detection of nanoscale vibrations based on sweep frequency (SF) - amplitude modulation (AM) method using cantilever sweep frequency as a carrier frequency was proposed. Furthermore, the process of tip-sample vibration coupling is analyzed by using the idea of amplitude modulation model. The nanoscle vibration detecting experiments were carried out on a piezoceramic disc, which can mimic cell vibrations. The experiment results show that the SF-AM AFM real-time vibration tracking and detecting approach can accurately detect and track feeble sample vibration within few nanometers amplitude.

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AAAI Conference 2015 Conference Paper

Resolving Over-Constrained Probabilistic Temporal Problems through Chance Constraint Relaxation

Peng Yu
Cheng Fang
Brian Williams

When scheduling tasks for field-deployable systems, our solutions must be robust to the uncertainty inherent in the real world. Although human intuition is trusted to balance reward and risk, humans perform poorly in risk assessment at the scale and complexity of real world problems. In this paper, we present a decision aid system that helps human operators diagnose the source of risk and manage uncertainty in temporal problems. The core of the system is a conflict-directed relaxation algorithm, called Conflict-Directed Chance-constraint Relaxation (CDCR), which specializes in resolving overconstrained temporal problems with probabilistic durations and a chance constraint bounding the risk of failure. Given a temporal problem with uncertain duration, CDCR proposes execution strategies that operate at acceptable risk levels and pinpoints the source of risk. If no such strategy can be found that meets the chance constraint, it can help humans to repair the overconstrained problem by trading off between desirability of solution and acceptable risk levels. The decision aid has been incorporated in a mission advisory system for assisting oceanographers to schedule activities in deepsea expeditions, and demonstrated its effectiveness in scenarios with realistic uncertainty.

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ICAPS Conference 2015 Conference Paper

Robust Execution of Plans for Human-Robot Teams

Erez Karpas
Steven James Levine
Peng Yu
Brian Williams 0001

Humans and robots working together can efficiently complete tasks that are very difficult for either to accomplish alone. To collaborate fluidly, robots must recognize the humans' intentions and adapt to their actions appropriately. Pike is an online executive introduced previously in the literature that unifies intent recognition and plan adaptation for temporally flexible plans with choice. While successful at coordinating human-robot teams, Pike had limited robustness to temporal uncertainty about the durations of actions. This paper presents two extensions to Pike that make it much more robust to temporal uncertainty. First, we extend Pike to handle uncontrollable action durations by enforcing strong temporal controllability. We accomplish this by generalizing standard strong controllability algorithms for STNUs to plans with choice. Second, in case a realized duration exceeds even the specified bounds and makes the entire plan infeasible, we attempt to intelligently negotiate with a human to relax some of the temporal constraints and restore feasibility, rather than immediately failing and halting execution. This negotiation is guided by a state-of-the-art conflict directed relaxation algorithm, which has previously only been used offline.

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AAAI Conference 2015 Conference Paper

Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease

Shandian Zhe
Zenglin Xu
Yuan Qi
Peng Yu

In the analysis and diagnosis of many diseases, such as the Alzheimer’s disease (AD), two important and related tasks are usually required: i) selecting genetic and phenotypical markers for diagnosis, and ii) identifying associations between genetic and phenotypical features. While previous studies treat these two tasks separately, they are tightly coupled due to the same underlying biological basis. To harness their potential benefits for each other, we propose a new sparse Bayesian approach to jointly carry out the two important and related tasks. In our approach, we extract common latent features from different data sources by sparse projection matrices and then use the latent features to predict disease severity levels; in return, the disease status can guide the learning of sparse projection matrices, which not only reveal interactions between data sources but also select groups of related biomarkers. In order to boost the learning of sparse projection matrices, we further incorporate graph Laplacian priors encoding the valuable linkage disequilibrium (LD) information. To efficiently estimate the model, we develop a variational inference algorithm. Analysis on an imaging genetics dataset for AD study shows that our model discovers biologically meaningful associations between single nucleotide polymorphisms (SNPs) and magnetic resonance imaging (MRI) features, and achieves significantly higher accuracy for predicting ordinal AD stages than competitive methods.

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AAAI Conference 2014 Conference Paper

Chance-Constrained Probabilistic Simple Temporal Problems

Cheng Fang
Peng Yu
Brian Williams

Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maximizing utility subject to these risk bounds. In this paper we present the probabilistic Simple Temporal Network (pSTN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for pSTNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound.

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ICAPS Conference 2014 Conference Paper

Resolving Uncontrollable Conditional Temporal Problems Using Continuous Relaxations

Peng Yu
Cheng Fang
Brian Williams 0001

Uncertainty is commonly encountered in temporal scheduling and planning problems, and can often lead to over-constrained situations. Previous relaxation algorithms for over-constrained temporal problems only work with requirement constraints, whose outcomes can be controlled by the agents. When applied to uncontrollable durations, these algorithms may only satisfy a subset of the random outcomes and hence their relaxations may fail during execution. In this paper, we present a new relaxation algorithm, Conflict-Directed Relaxation with Uncertainty (CDRU), which generates relaxations that restore the controllability of conditional temporal problems with uncontrollable durations. CDRU extends the Best-first Conflict-Directed Relaxation (BCDR) algorithm to uncontrollable temporal problems. It generalizes the conflict-learning process to extract conflicts from strong and dynamic controllability checking algorithms, and resolves the conflicts by both relaxing constraints and tightening uncontrollable durations. Empirical test results on a range of trip scheduling problems show that CDRU is efficient in resolving large scale uncontrollable problems: computing strongly controllable relaxations takes the same order of magnitude in time compared to consistent relaxations that do not account for uncontrollable durations. While computing dynamically controllable relaxations takes two orders of magnitude more time, it provides significant improvements in solution quality when compared to strongly controllable relaxations.

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IJCAI Conference 2013 Conference Paper

Continuously Relaxing Over-Constrained Conditional Temporal Problems through Generalized Conflict Learning and Resolution

Peng Yu
Brian Williams

Over-constrained temporal problems are commonly encountered while operating autonomous and decision support systems. An intelligent system must learn a human’s preference over a problem in order to generate preferred resolutions that minimize perturbation. We present the Best-first Conflict-Directed Relaxation (BCDR) algorithm for enumerating the best continuous relaxation for an over-constrained conditional temporal problem with controllable choices. BCDR reformulates such a problem by making its temporal constraints relaxable and solves the problem using a conflictdirected approach. It extends the Conflict-Directed A* (CD-A*) algorithm to conditional temporal problems, by first generalizing the conflict learning process to include all discrete variable assignments and continuous temporal constraints, and then by guiding the forward search away from known infeasible regions using conflict resolution. When evaluated empirically on a range of coordinated car sharing network problems, BCDR demonstrates a substantial improvement in performance and solution quality compared to previous conflict-directed approaches.

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YNIMG Journal 2010 Journal Article

Altered white matter microstructure in the corpus callosum in Huntington's disease: Implications for cortical “disconnection”

H. Diana Rosas
Stephanie Y. Lee
Alexander C. Bender
Alexandra K. Zaleta
Mark Vangel
Peng Yu
Bruce Fischl
Vasanth Pappu

The corpus callosum (CC) is the major conduit for information transfer between the cerebral hemispheres and plays an integral role in relaying sensory, motor and cognitive information between homologous cortical regions. The majority of fibers that make up the CC arise from large pyramidal neurons in layers III and V, which project contra-laterally. These neurons degenerate in Huntington's disease (HD) in a topographically and temporally selective way. Since any focus of cortical degeneration could be expected to secondarily de-afferent homologous regions of cortex, we hypothesized that regionally selective cortical degeneration would be reflected in regionally selective degeneration of the CC. We used conventional T1-weighted, diffusion tensor imaging (DTI), and a modified corpus callosum segmentation scheme to examine the CC in healthy controls, huntingtin gene-carriers and symptomatic HD subjects. We measured mid-sagittal callosal cross-sectional thickness and several DTI parameters, including fractional anisotropy (FA), which reflects the degree of white matter organization, radial diffusivity, a suggested index of myelin integrity, and axial diffusivity, a suggested index of axonal damage of the CC. We found a topologically selective pattern of alterations in these measures in pre-manifest subjects that were more extensive in early symptomatic HD subjects and that correlated with performance on distinct cognitive measures, suggesting an important role for disrupted inter-hemispheric transfer in the clinical symptoms of HD. Our findings provide evidence for early degeneration of commissural pyramidal neurons in the neocortex, loss of cortico-cortical connectivity, and functional compromise of associative cortical processing.

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