Author name cluster

He Xu

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.

8 papers

2 author rows

EAAI Journal 2025 Journal Article

Enhancing diabetes complications prediction through knowledge graphs and convolutional networks

Haitao Cheng
Qunli Zheng
Peng Li
He Xu

Early prediction of diabetes complications is crucial for timely intervention and effective disease management. However, current deep learning approaches often lack sufficient representation of diabetes knowledge and rely on large-scale annotated data. To address this, we propose a Knowledge Graphs-enhanced Diabetes Complications Prediction (KGDCP) model, which integrates a physical examination knowledge graph and an improved Diabetes Knowledge Graph (DiaKG) using self-attention-based convolutional neural networks. We construct the physical examination knowledge graph based on normal reference ranges of examination indicators and enhance DiaKG through knowledge deduplication and coreference resolution, improving its accuracy and comprehensiveness. Furthermore, we employ the embeddings of these two knowledge graphs to represent examination data and diagnostic information. A token-to-token (token2token) self-attention is utilized to explore dependencies among examination indicators, and convolutional neural networks extract local features to generate representation vectors. Additionally, source-to-token (source2token) self-attention assesses dependencies between diagnostic entities and the entire entity set, with entity-to-Physical Examination (entity2PE) attention gauging entity relevance to examination data representation vectors. Our model combines these features to enhance diabetes complications prediction, outperforming traditional models in predicting cerebral infarction and peripheral neuropathy.

Details DOI

ICRA Conference 2023 Conference Paper

An Underwater Jet-Propulsion Soft Robot with High Flexibility Driven by Water Hydraulics

Siqing Chen
He Xu
Xiao Xiong
Ben Lu

Compared with rigid robots, soft robots have the advantages of inherent compliance, high adaptability, and impact tolerance. Many researchers are very interested in the motion design of soft robot underwater. In this paper, inspired by the method of octopus propulsion, a jet propulsion unit with 80% soft materials driven by pressure is designed. It can change the volume of its cavity to absorb and eject the fluid medium to make the robot move. According to the working characteristics of the jet unit, corresponding experiments are designed to analyze its force output, deformation, ejection flow, and pressure response characteristics. In order to expand the motion space of the robot, a buoyancy unit is designed to control the depth of the robot in the water. Three jet units and a buoyancy element are combined into a tetrahedron robot - jet soft robot (JSR). The feasibility of its motion is verified by experiments. Compared with other similar jet robots, the biggest feature of this robot is that the drive unit can bend or twist roughly along the centerline, which can prevent accidental collision and damage.

Details

EAAI Journal 2023 Journal Article

Combining knowledge extension with convolution neural network for diabetes prediction

Haitao Cheng
Jingshu Zhu
Peng Li
He Xu

Prediction and diagnosis of diabetes are critical issues in the field of smart healthcare. However, the dependence of large-scale annotated diabetes data and the lack of diabetes knowledge represent significant challenges for diabetes prediction. To address these challenges, we propose a new diabetes prediction model named KE-CNN, which combines knowledge extension and convolution neural network. The KE-CNN model first extracts abnormal indicator features from physical examination index data of diabetic patients and uses Word2vec to embed the feature words. We then employ entity recognition technique named BERT-BiLSTM-CRF to identify medical entities in the condition description text and utilize a knowledge graph to extend the knowledge of each medical entity, followed by using pre-trained Chinese word vectors to embed the extended description text. Finally, we construct a semantic enhanced convolutional neural network model with word embedding vectors and text embedding vectors as dual-channel input, aiming to enhance the feature expression of the KE-CNN model. Our model not only learns and captures more fine-grained features of diabetes information, but also significantly reduces the amount of data required for model training and improves the prediction performance of convolutional neural network models. Our experiments show that the KE-CNN model effectively improves the accuracy of diabetes prediction compared with the benchmark model.

Details DOI

IROS Conference 2022 Conference Paper

Multi-DoF Soft Robotic Actuators Based on Spring Reinforce and Particle Jamming

Weiwang Fan
He Xu
Haihang Wang
Siqing Chen
Qiandiao Wei
Chaochao You

Soft robots have a wide rang of applications due to their compliance, flexibility and low fabrication cost. Compare to rigid robots, soft robots are more safe for human. In this work, we design various multi-DoF actuators with spring reinforce and particle jamming, and two fabrication methods are proposed to make them. Each type of actuator is tested to evaluate the mechanical properties by experiments. Experimental results show that both spring and particle jamming have effect on the stiffness. Besides, Spring reinforced actuator (SRA) increases the maximum allowable inflation pressure, output bending force and gives a good linear relationship of bending. We also integrate spring and particles to fabricate a hybrid actuator whose behaviors are explored by experiments. Finally, we create a snake-like robotic manipulator assembled with two actuators and show its bending motion. Results show that the manipulator is able to achieve several bending shapes steadily and large range of motion.

Details

EAAI Journal 2021 Journal Article

Arrangement optimization of a novel three dimensional multiphase flow imaging device employing modified harmony search algorithm

Haihang Wang
He Xu
Xiao-Zhi Gao
Zitong Zhao
Jinwei Huang

Gas–liquid two-phase flow is a typical flow. Its bubble characteristic measurement is of great importance on studying the flow mechanism and guiding the practical fluid mechanical engineering. In this paper, a novel three dimensional (3D) multiphase flow imaging device was designed and optimized to measure the transparent object that has an opaque object in the center of the 3D observed area. Its mathematical model was built and the constraints were defined based on the geometrical relationship and design requirements. Based on the original harmony search (HS) algorithm, a modified harmony search algorithm (MHS-MC) for improving both the performance on the global optima of the multi-modal problems and the convergence performance of the constrained optimization problems was integrated and applied to optimize the arrangement of the single-camera-multi-mirror device. As a case study, the 3D multiphase flow imaging method was applied in the 3D reconstruction of the cavitation bubble cluster inside a water hydraulic valve. The statistics of the Pareto data show the good performance of the MHS-MC algorithm. And the cavitation experimental results testify the effectiveness of the proposed MHS-MC algorithm. The cavitation bubble cluster can be reconstructed with quite high precision.

Details DOI

ICRA Conference 2020 Conference Paper

Modeling and Experiments on the Swallowing and Disgorging Characteristics of an Underwater Continuum Manipulator

Haihang Wang
He Xu
Fengshu Yu
Xin Li
Chen Yang
Siqing Chen
Junlong Chen
Yonghui Zhang

Soft robots apply compliant materials to perform motions and behaviors not typically achievable by rigid robots. An underwater, compliant, multi-segment continuum manipulator that can bend, swallow, disgorge is developed in this study. The manipulator is driven by McKibben water hydraulic artificial muscle (WHAM). The mechanical properties of the WHAM are tested and analyzed experimentally. The kinematics model, which concerns about the variable diameter structure of the soft grippers, are established to simulate the behaviors of the manipulator among the bending, swallowing and disgorging procedure. A mouth-tongue collaborative soft robot assembled with another single-segment soft robot arm is presented. And its functions are experimentally testified. The distinctive functions were verified according to the experimental results.

Details

RLDM Conference 2017 Conference Abstract

Humans utilize an eligibility trace when learning sequential decisions from reward

Marco Lehmann
He Xu
Vasiliki Liakoni
Wulfram Gerstner
Kerstin Preuschoff

Whether we prepare a coffee or navigate to a shop: in many tasks we make multiple decisions before reaching a goal. Learning such state-action sequences from sparse reward raises the problem of credit-assignment: which actions out of a long sequence should be reinforced? One solution provided by reinforcement learning (RL) theory is the eligibility trace (ET); a decaying memory of the state-action history. Here we investigate behaviorally and neurally whether humans utilize an ET when learning a multi- step decision making task. We implemented three versions of a novel task using visual, acoustic, and spatial cues. Eleven subjects performed all three conditions while we recorded their pupil diameter. We considered model-based and model-free (with and without ET) algorithms to explain human learning. Using the Akaike Information Criterion (AIC) we find that model-free learning with ET explains the human behavior best in all three conditions. Cross-validation confirm this behavioral result. We then compare pupil dilation in early and late learning and observe differences that are consistent with an ET contribution. In particular, we find significant changes in pupil response to non-goal states after just a single reward in all three experimental conditions. In this research we introduce a novel paradigm to study the ET in human learning in a multi- step sequential decision making task. The analysis of the behavioral and pupil data provides evidence that humans utilize an eligibility trace to solve the credit-assignment problem when learning from sparse and delayed reward.

PDF Details

IROS Conference 2006 Conference Paper

Avoiding Static and Dynamic Objects in Navigation

Han Li
Yili Fu
He Xu
Yulin Ma

Real-time collision free path planning involves avoidance of static as well as dynamic objects in unknown environment. Strategies suitable for stationary navigation cannot be suitable for the dynamic environment. Behavior-based control combined with fuzzy control to avoid dynamic and static obstacle is described in this paper. Behavior-based control helps the robot get over complex static environment or avoid dynamic objects according to different collision situation. Double-layered fuzzy logic control helps figure out velocity and steering angle of the robot based on some uncertain information. The method has been tested effectively through simulation by a mobile robot navigating amidst multiple static and dynamic environments

Details