Author name cluster

Yan Shi

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.

7 papers

1 author row

EAAI Journal 2026 Journal Article

An autoregressive framework for reconstructing editable parametric computer-aided design models from point clouds

Jiaxing Lu
Yixuan Wang
Yangyong Wu
Heran Li
Yan Shi
Fangwei Ning

Reverse engineering of industrial parts from three-dimensional (3D) point clouds is vital for modern computer-aided design (CAD) workflows in manufacturing and design. However, the unordered nature of point clouds and its substantial modality gap from sequential, parameterized CAD modeling make accurate and editable reconstruction particularly challenging. Existing methods often struggle to address both issues simultaneously, resulting in limited shape fidelity and practical usability. This work introduces an end-to-end autoregressive transformer-based framework termed RenCAD, which directly maps point cloud representations to CAD modeling sequences, enabling automated, parametric reconstruction. The primary artificial intelligence (AI) contribution of RenCAD lies in its overall autoregressive architecture, which generates CAD sequences token by token to capture inter-command dependencies. This is further enhanced by a group-based tokenization mechanism with learnable positional encoding to effectively handle unordered point clouds, and a proportional reconstruction loss that improves parameter learning with respect to geometric accuracy. From an engineering perspective, RenCAD reduces manual modeling effort while preserving editability and precision. It outputs parametric operations fully compatible with standard CAD software, supporting seamless integration into industrial workflows. Extensive experiments on a public benchmark demonstrate that RenCAD outperforms similar methods across multiple evaluation metrics, achieving higher command accuracy, lower invalid rates, and improved geometric fidelity. These results confirm the robustness and scalability of RenCAD, advancing AI-driven CAD automation by tightly coupling raw point cloud data with structured modeling logic. Nevertheless, RenCAD only supports a limited set of modeling operations. Future work will extend its modeling capabilities and adaptability to broader industrial scenarios.

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EAAI Journal 2026 Journal Article

Temporal feature mixed inverted transformer: An inverted transformer for effective real-time electricity price forecasting

Baichun Wang
Baoxian Huang
Qinglun Zhang
Yan Shi
Hong Men

Real-time electricity prices reflect the instantaneous balance between supply and demand in the electricity market and serve as a critical mechanism for enabling market-based dispatch and promoting the integration of renewable energy. Accurate forecasting of real-time prices facilitates the formulation of optimal trading strategies for electricity retailers and plays a vital role in enhancing the operational efficiency and stability of electricity markets. However, due to the highly volatile nature and complex underlying dynamics of real-time prices, forecasting remains a significant challenge. To address this issue, this study introduces the Inverted Transformer (iTransformer) and proposes an innovative hybrid forecasting model designed to achieve high-precision electricity price predictions. The proposed model integrates price volatility indicators and rate-of-change metrics to effectively capture subtle temporal dependencies across multi-scale feature sequences. Furthermore, the model incorporates future covariates in an autoregressive framework. This provides additional auxiliary information and alleviates the limitations of approaches that rely only on historical data. The model is empirically validated using real-world data from the electricity spot market in Shandong Province, China. Comparative experimental results demonstrate that the proposed hybrid model achieves superior forecasting accuracy, with a minimum Mean Squared Error (MSE) of 0. 0134, Mean Absolute Error (MAE) of 0. 0816, Root Mean Squared Error (RMSE) of 0. 1157, and Mean Absolute Percentage Error (MAPE) of 0. 9547. These findings indicate that the proposed model significantly enhances the predictive accuracy of real-time electricity prices and demonstrates practical applicability in real-world electricity market operations, with clear real-world significance for improving the stability of electricity markets.

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JBHI Journal 2025 Journal Article

Design of a Multi-Parameter Fusion Sensor and System for Respiratory Monitoring of Mechanically Ventilated Patients in the ICU

Shuai Ren
Xiaohan Wang
Maolin Cai
Yan Shi
Tao Wang
Zujin Luo

In order to achieve precise respiratory therapy for mechanically ventilated patients, real-time monitoring of the state parameters of inhaled and exhaled gases is required. These parameters are primarily measured by ventilators, with limitations such as insufficient monitoring parameters, circuit leaks, and constraints imposed by distance and obstacles. This paper designs a low-power wireless sensor for multi-parameter monitoring near the patient, which can be used continuously for approximately 60 days. Based on this sensor, an intelligent respiratory monitoring system with a distributed architecture is proposed to achieve intelligent patient-ventilator asynchrony (PVA) perception. Experimental results show that the system can stably and accurately collect and transmit data, with measurement errors for pressure, flow, temperature, humidity, and CO $_{2}$ concentration being $\pm$ 1. 3%, $\pm$ 2. 1%, $\pm$ 0. 6 $^\circ$, $\pm$ 1% RH, $\pm$ 0. 3 mmHg respectively. The proposed sensor and system have the potential to enhance the efficiency and intelligence of medical care significantly.

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JBHI Journal 2025 Journal Article

Improving Patient-Ventilator Synchrony During Pressure Support Ventilation Based on Reinforcement Learning Algorithm

Liming Hao
Xiaohan Wang
Shuai Ren
Yan Shi
Maolin Cai
Tao Wang
Zujin Luo

Mechanical ventilation is an effective treatment for critically ill patients and those with pulmonary diseases. However, patient-ventilator asynchrony (PVA) remains a significant challenge, potentially leading to high mortality. Improving patient-ventilator synchrony poses a complex decision-making problem in clinical practice. Traditional methods rely heavily on clinicians' experience, often resulting in inefficiencies, delayed ventilator adjustments, and resource shortages. This paper proposes a novel approach using a deep reinforcement learning (RL) algorithm based on deep Q-learning (DQN) to enhance patient-ventilator synchrony during pressure support ventilation. The action space and reward function are established from clinical experience, and a pneumatic model of the mechanical ventilation system is constructed to simulate various patient conditions and types of PVAs. Clinical data are used to evaluate the RL algorithm qualitatively and quantitatively. The RL-optimized ventilation strategy reduces the proportion of breaths containing PVAs from 37. 52% to 7. 08%, demonstrating its effectiveness in assisting clinical decision-making, improving synchrony, and enabling intelligent ventilator control, bedside monitoring, and automatic weaning.

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EAAI Journal 2025 Journal Article

Neural network adaptive force control for pneumatic polishing end-actuator with external disturbances and full-state constrains

Zhiguo Yang
Jiange Kou
Zhanxin Li
Wenbo Zhao
Yushan Ma
Yixuan Wang
Yan Shi

In pneumatic polishing, the nonlinear, time-varying, and uncertain contact characteristics introduce significant modeling inaccuracies, posing substantial challenges to the realization of precise and robust force control. This paper proposes a neural-network adaptive force control strategy for a pneumatic polishing end-actuator under external disturbances and full-state constraints. To estimate the unmeasurable states and enhance the ant disturbance capability, a composite observer is developed to estimate the internal states and external disturbances in real time. Under the adaptive backstepping design framework, a radial-basis-function–neural-network–based adaptive learning mechanism is employed to approximate the nonlinear uncertainties, and a dynamic surface-control structure is introduced to avoid the complexity explosion in conventional recursive designs. Furthermore, a barrier Lyapunov function is integrated to ensure compliance with the full-state constraints throughout the control process. The convergence of the controller is verified through stability analyses, and the effectiveness and superiority of the control scheme is verified via experiments in four different polishing scenarios. The results show that the proposed control method achieves an average force tracking error less than 0. 07 N and convergence time less than 2. 35 s, showing higher control accuracy, faster transient response, and stronger robustness, than similar control algorithms.

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JBHI Journal 2024 Journal Article

Detection of Coronary Artery Disease Based on Clinical Phonocardiogram and Multiscale Attention Convolutional Compression Network

Chongbo Yin
Yineng Zheng
Xiaorong Ding
Yan Shi
Jian Qin
Xingming Guo

Heart sound is an important physiological signal that contains rich pathological information related with coronary stenosis. Thus, some machine learning methods are developed to detect coronary artery disease (CAD) based on phonocardiogram (PCG). However, current methods lack sufficient clinical dataset and fail to achieve efficient feature utilization. Besides, the methods require complex processing steps including empirical feature extraction and classifier design. To achieve efficient CAD detection, we propose the multiscale attention convolutional compression network (MACCN) based on clinical PCG dataset. Firstly, PCG dataset including 102 CAD subjects and 82 non-CAD subjects was established. Then, a multiscale convolution structure was developed to catch comprehensive heart sound features and a channel attention module was developed to enhance key features in multiscale attention convolutional block (MACB). Finally, a separate downsampling block was proposed to reduce feature losses. MACCN combining the blocks can automatically extract features without empirical and manual feature selection. It obtains good classification results with accuracy 93. 43%, sensitivity 93. 44%, precision 93. 48%, and F1 score 93. 42%. The study implies that MACCN performs effective PCG feature mining aiming for CAD detection. Further, it integrates feature extraction and classification and provides a simplified PCG processing case.

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TCS Journal 2011 Journal Article

On positive influence dominating sets in social networks

Feng Wang
Hongwei Du
Erika Camacho
Kuai Xu
Wonjun Lee
Yan Shi
Shan Shan

In this paper, we investigate the positive influence dominating set (PIDS) which has applications in social networks. We prove that PIDS is APX-hard and propose a greedy algorithm with an approximation ratio of H ( δ ) where H is the harmonic function and δ is the maximum vertex degree of the graph representing a social network.

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