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Jie Yao

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3 papers

2 author rows

JBHI Journal 2025 Journal Article

Estimation of Ankle Joint Moment From Plantar Pressure Through an Optimized Sensor Layout Using Genetic Algorithm and Deep Forest Regression

Mingxia Gong
Wenxuan Chen
Yih-Kuen Jan
Yu Zhao
Jie Yao
Yan Wang
Weiyan Ren
Fang Pu

Objective: Ankle joint moments are critical in gait analysis, with accurate assessments typically necessitating complex inverse dynamics modeling. Pressure insoles are widely used wearable devices that have shown feasibility in estimating joint angles. However, achieving cost-effective, high-precision estimation of ankle joint moment remains challenging. This study combines genetic algorithm (GA) with deep forest regression (DFR) to optimize the number and layout of plantar pressure sensors, and estimate ankle joint moment based on plantar pressure. Methods: 26 healthy young participants were recruited to collect motion trajectories, ground reaction forces, and plantar pressure data while walking at fast, medium, and slow speeds. Ten gait cycles per speed per participant were analyzed for ankle joint moments using inverse dynamics, constituting the dataset. An optimization algorithm was constructed by combining GA with DFR, using the fitness function as the objective for sensor number and layout optimization. The leave-one-out cross-validation was employed to evaluate the precision of the model. Results: The highest fitness was achieved with an optimized layout using 9 sensors. The Pearson Correlation Coefficients for the sagittal, coronal, and transverse plane moments were 0. 967 ± 0. 014, 0. 918 ± 0. 027, and 0. 894 ± 0. 073. The optimized layout showed no significant difference in estimation accuracy across various walking speeds (P > 0. 05). Conclusion: The proposed GA-DFR algorithm is capable of estimating ankle joint moment accurately and optimizing the number and layout of sensors. Significance: The algorithm and optimized sensor layout enables the accurate and rapid estimation of ankle joint moment from plantar pressure insoles with trade-off approach.

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AAAI Conference 2024 Conference Paper

MathAttack: Attacking Large Language Models towards Math Solving Ability

Zihao Zhou
Qiufeng Wang
Mingyu Jin
Jie Yao
Jianan Ye
Wei Liu
Wei Wang
Xiaowei Huang

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the robustness of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of robustness in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. The code and dataset is available at: https://github.com/zhouzihao501/MathAttack.

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IROS Conference 2023 Conference Paper

Leader-Follower Formation Control of a Large-Scale Swarm of Satellite System Using the State-Dependent Riccati Equation: Orbit-to-Orbit and In-Same-Orbit Regulation

Saeed Rafee Nekoo
Jie Yao
Alejandro Suárez
Raul Tapia
Aníbal Ollero

The state-dependent Riccati equation (SDRE) is a nonlinear optimal controller with a flexible structure which is one of the main advantages of this method. Here in this work, this flexibility is used to present a novel design for handling a soft constraint for state variables (trajectories). The concept is applied to a large-scale swarm control system, with more than 1000 agents. The control of the swarm satellite system is devoted to two modes of orbit-to-orbit and in-same-orbit cases. Keeping the satellites in one orbit in regulation (point-to-point motion) requires additional constraints while they are moving in Cartesian coordinates. For a small number of agents trajectory design could be done for each satellite individually, though, for a swarm with many agents, that is not practical. The constraint has been incorporated into the cost function of optimal control and resulted in a modified SDRE control law. The proposed method successfully controlled a swarm case of 1024 agents in leader-follower mode for orbit-to-orbit and in-same-orbit simulations. The soft constraint presented a percentage of 0. 05 in the error of the satellites with respect to travel distance, in in-same-orbit regulation. The presented approach is systematic and could be performed for larger swarm systems with different agents and dynamics.

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