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Yifan Li

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20 papers

2 author rows

EAAI Journal 2026 Journal Article

Aircraft geomagnetic navigation via dual-view feature extraction and hybrid multi-criteria adaptive weighting

Yifan Li
Zihao Chen
Mingqi Lv
Tieming Chen
Baiyang Ji

Geomagnetic navigation is a passive technique that leverages the spatial distribution of the Earth’s magnetic field to mitigate the susceptibility of Global Navigation Satellite Systems (GNSS) to external interference and reduce cumulative errors in inertial navigation systems, thereby ensuring robust stability. However, its relatively low accuracy has historically limited practical deployment. To address this challenge, this paper proposes an aircraft geomagnetic navigation method via dual-view feature extraction and hybrid multi-criteria adaptive weighting (DHAGN). DHAGN extracts features from two distinct views, adaptively adjusts feature weights using both standard-deviation-based and summation-based criteria, and integrates an loss-feedback mechanism within the summation-based weighting to further enhance navigation accuracy. Experiments on 13 flight routes from the SGL2020 dataset demonstrate that DHAGN achieves an average distance-root-mean-square (DRMS) error reduction of 45. 5 meters compared to the state-of-the-art Magnav2C method, validating its effectiveness in enhancing geomagnetic navigation accuracy and facilitating practical implementation.

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AAAI Conference 2026 Conference Paper

Analyzing and Mitigating Object Hallucination: A Training Bias Perspective

Yifan Li
Kun Zhou
Xin Zhao
Lei Fang
Jirong Wen

As scaling up training data has significantly improved the general multimodal capabilities of Large Vision-Language Models (LVLMs), they still suffer from the hallucination issue, generating text that is inconsistent with the visual input. This phenomenon motivates us to systematically investigate the role of training data in hallucination. We introduce a new benchmark, POPEv2, which consists of counterfactual images collected from the training data of LVLMs with certain objects masked. Through comprehensive evaluation on POPEv2, we find that current LVLMs suffer from training bias: they fail to fully leverage their training data and hallucinate more frequently on images seen during training. Specifically, they perform poorly on counterfactual images, often incorrectly answering “Yes” to questions about masked objects. To understand this issue, we conduct probing experiments on the models’ internal components, revealing that this training bias is primarily located in the language modeling (LM) head, which fails to correctly translate accurate visual representations into textual outputs. Based on these findings, we propose Obliviate, an efficient and lightweight unlearning method designed to mitigate object hallucination via training bias unlearning. Obliviate identifies the discrepancy between ground-truth labels and model outputs on the training data as a proxy for bias and adopts a parameter- and data-efficient fine-tuning strategy that only updates the LM head. Extensive experiments demonstrate the effectiveness of our approach. While only reusing the training data and updating approximately 2% of the parameters, Obliviate significantly reduces hallucination across both discriminative and generative tasks. Furthermore, it demonstrates strong scalability with respect to both model size (2B to 72B) and training data volume, and exhibits promising generalization to hallucination types beyond object-level hallucination.

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AAAI Conference 2026 Conference Paper

LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning

Liutao
Xutao Mao
Dixuan Zhang
Yifan Li
LiuHaixin
KongLulu
Jiaming Hou
Rui Li

Text-to-SQL is a critical task in natural language processing that aims to transform natural language questions into accurate and executable SQL queries. In real-world scenarios, these reasoning tasks are often accompanied by complex mathematical computations, domain knowledge, and hypothetical reasoning scenarios. However, existing large-scale Text-to-SQL datasets typically focus on business logic and task logic, neglecting critical factors such as vertical domain knowledge, complex mathematical reasoning, and hypothetical reasoning, which are essential for realistically reflecting the reasoning demands in practical applications and completing data querying and analysis. To bridge this gap, we introduce LogicCat, the first Text-to-SQL benchmark dataset specifically designed for complex reasoning and chain-of-thought parsing, encompassing physics, arithmetic, commonsense, and hypothetical reasoning scenarios. LogicCat comprises 4,038 English questions paired 12,114 detailed chain-of-thought reasoning steps, spanning 45 databases across diverse domains, significantly surpassing existing datasets in complexity. Experimental results demonstrate that LogicCat substantially increases the task difficulty for current state-of-the-art models to at most 33.20% execution accuracy, indicating that this task remains exceptionally challenging. The advancement of LogicCat represents a crucial step toward developing systems suitable for real-world enterprise data analysis and autonomous query generation.

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JBHI Journal 2025 Journal Article

An Interpretable AI for Smart Homes: Identifying Fall Prevention Strategies for Older Adults Using Multimodal Deep Learning

Jeongyeop Baek
Yifan Li
Lisa Lim
Jo Woon Chong

Falls are a significant cause of mortality among older adults and are considered preventable emergencies. We developed an interpretation framework, including multimodal predictive models that capture both static and time-series data, and analyzed global feature importance via a perturbation approach, permutation importance (PIMP), and SHapley Additive exPlanations (SHAP) to identify fall prevention strategies at home. Our predictive model utilizing the BiCrossNet architecture achieved a prediction accuracy of 98% when 12, 540 data points were used. It was found that activity-related and indoor thermal environment features are important for predicting fall emergency occurrences (FEOs). Interestingly, extended active time in the living room/kitchen and outdoors decreases the FEO ratio, whereas extended active time in the bedroom and bathroom increases the FEO ratio. For example, increasing the active time in the living room/kitchen from 60−70 to 130−140 minutes reduced the FEO ratio by 32%. Furthermore, indoor temperature and humidity were found to be important depending on the outdoor conditions. Moreover, this study shows the importance of personalized prevention strategies depending on the underlying health conditions of older adults.

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EAAI Journal 2025 Journal Article

Continuous spatio temporal prompts for visual tracking

Meng Sun
Xiaotao Liu
Yifan Li
Hongyu Wang
Dian Yuan
Jing Liu

Currently, visual single-object tracking methods utilize online template updates to combine temporal information. However, these methods rely on confidence scores to evaluate the reliability of the current template, which may result in a template not being updated for an extended period. Moreover, advanced trackers select bounding boxes based solely on the similarity between the template and the search area, which can lead to tracking drift when encountering deformable or similar targets. To alleviate these limitations, we propose a Spatio Temporal Prompt Tracker (STPTrack), which utilizes the prior information about small changes of object state between successive frames. Different from previous tracking methods that mainly rely on templates and similarity scores, STPTrack transfers the object position and shape information of the previous frame to the current frame as continuous spatio temporal prompt for the first time, and realizes the efficient fusion of spatio temporal information through the prompt encoder and the fusion decoder module. Specifically, it encodes the bounding box coordinates or mask information of the previous frame and the response points of the current frame as prompt features, and then combines prompt tokens with search tokens through the fusion decoder to provide the potential location of the object for the search feature map. Our STPTrack sets a new state-of-the-art performance on six tracking benchmark datasets.

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NeurIPS Conference 2025 Conference Paper

Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness

Rongzhe Wei
Peizhi Niu
Hans Hao-Hsun Hsu
Ruihan Wu
Haoteng YIN
Mohsen Ghassemi
Yifan Li
Vamsi Potluru

Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of isolated facts, often overlooking latent inferential dependencies and the non-deterministic nature of knowledge within LLMs. Consequently, facts presumed forgotten may persist implicitly through correlated information. To address these challenges, we propose a knowledge unlearning evaluation framework that more accurately captures the implicit structure of real-world knowledge by representing relevant factual contexts as knowledge graphs with associated confidence scores. We further develop an inference-based evaluation protocol leveraging powerful LLMs as judges; these judges reason over the extracted knowledge subgraph to determine unlearning success. Our LLM judges utilize carefully designed prompts and are calibrated against human evaluations to ensure their trustworthiness and stability. Extensive experiments on our newly constructed benchmark demonstrate that our framework provides a more realistic and rigorous assessment of unlearning performance. Moreover, our findings reveal that current evaluation strategies tend to overestimate unlearning effectiveness.

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NeurIPS Conference 2025 Conference Paper

IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios

Yifan Li
Yuhang Chen
Anh Dao
Lichi Li
Zhongyi Cai
Zhen Tan
Tianlong Chen
Yu Kong

Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to industrial settings. This drawback limits the evaluation of agent readiness for real-world industrial applications. To bridge this, we introduce IndustryEQA, the first benchmark dedicated to evaluating embodied agent capabilities within safety-critical industrial warehouse scenarios. Built upon the NVIDIA Isaac Sim platform, IndustryEQA provides high-fidelity episodic memory videos featuring diverse industrial assets, dynamic human agents, and carefully designed hazardous situations inspired by real-world safety guidelines. The benchmark includes rich annotations covering six categories: equipment safety, human safety, object recognition, attribute recognition, temporal understanding, and spatial understanding. Besides, it also provides extra reasoning evaluation based on these categories. Specifically, it comprises 971 question-answer pairs generated from small warehouse scenarios and 373 pairs from large ones, incorporating scenarios with and without human. We further propose a comprehensive evaluation framework, including various baseline models, to assess their general perception and reasoning abilities in industrial environments. IndustryEQA aims to steer EQA research towards developing more robust, safety-aware, and practically applicable embodied agents for complex industrial environments.

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NeurIPS Conference 2025 Conference Paper

Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models

zhentao he
Can Zhang
Ziheng Wu
Zhenghao Chen
Yufei Zhan
Yifan Li
Zhao Zhang
Xian Wang

Recent advancements in multimodal large language models (MLLMs) have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompleteness within their paradigm in real-world scenarios, particularly under visual degradation (e. g. , blur, occlusion, low contrast). In such conditions, the current response paradigm often fails to adequately perceive visual degradation and ambiguity, leading to overreliance on linguistic priors or misaligned visual-textual reasoning. This difficulty in recognizing uncertainty frequently results in the generation of hallucinatory content, especially when a precise answer is not feasible. To better demonstrate and analyze this phenomenon and problem, we propose KIE-HVQA, the first benchmark dedicated to evaluating OCR hallucination in degraded document understanding. This dataset includes test samples spanning identity cards, invoices, and prescriptions, with simulated real-world degradations and pixel-level annotations for OCR reliability. This setup allows for evaluating models' capacity, under degraded input, to distinguish reliable visual information and answer accordingly, thereby highlighting the challenge of avoiding hallucination on uncertain data. To achieve vision-faithful reasoning and thereby avoid the aforementioned issues, we further introduce a Group Relative Policy Optimization (GRPO)-based framework featuring a novel reward mechanism. By incorporating a self-awareness of visual uncertainty and an analysis method that initiates refusal to answer to increase task difficulty within our supervised fine-tuning and reinforcement learning framework, we successfully mitigated hallucinations in ambiguous regions. Experiments on Qwen2. 5-VL demonstrate that our 7B-parameter model achieves a ~28% absolute improvement in hallucination-free accuracy over GPT-4o on KIE-HVQA and there is no significant performance drop in standard tasks, highlighting both effectiveness and robustness. This work advances the development of reliable MLLMs for real-world document analysis by addressing critical challenges in visual-linguistic alignment under degradation.

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ICML Conference 2025 Conference Paper

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning

Rongzhe Wei
Mufei Li
Mohsen Ghassemi
Eleonora Kreacic
Yifan Li
Xiang Yue
Bo Li 0026
Vamsi K. Potluru

Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its restrictive assumptions make it unsuitable for LLMs, giving rise to various heuristic approaches typically assessed through empirical evaluations. These standard evaluations randomly select data for removal, apply unlearning techniques, and use membership inference attacks (MIAs) to compare unlearned models against models retrained without the removed data. However, to ensure robust privacy protections for every data point, it is essential to account for scenarios in which certain data subsets face elevated risks. Prior research suggests that outliers, particularly including data tied to minority groups, often exhibit higher memorization propensity which indicates they may be more difficult to unlearn. Building on these insights, we introduce a complementary, minority-aware evaluation framework to highlight blind spots in existing frameworks. We substantiate our findings with carefully designed experiments, using canaries with personally identifiable information (PII) to represent these minority subsets and demonstrate that they suffer at least 20% higher privacy leakage across various unlearning methods, MIAs, datasets, and LLM scales. Our proposed minority-aware evaluation framework marks an essential step toward more equitable and comprehensive assessments of LLM unlearning efficacy.

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ICRA Conference 2024 Conference Paper

Adaptive Motion Scaling for Robot-Assisted Microsurgery Based on Hybrid Offline Reinforcement Learning and Damping Control

Peiyang Jiang
Wei Li 0105
Yifan Li
Dandan Zhang 0001

Motion scaling is essential to empower users to conduct precise manipulation during teleoperation for robot-assisted microsurgery (RAMS). A constant, small motion scaling ratio can enhance the precision of teleoperation but hinder the operator from quickly reaching distant targets. The concept of self-adaptive motion scaling has been proposed in previous work. However, previous frameworks required extensive manual tuning of core parameters, which significantly depends on prior knowledge and may potentially lead to non-optimal solutions. This paper presents a hybrid offline reinforcement learning and damping control approach to regulate the motion scaling ratio for different operations during offline training. This method can take user-specific characteristics into consideration and help them achieve better teleoperation performance. Comparisons are made with and without using the adaptive motion-scaling algorithm. Detailed user studies indicate that a suitable motion-scaling ratio can be obtained and adjusted online. The overall performance of the operators in terms of time cost for task completion is significantly improved, while the variance of average speed and the total distance for robot operation is reduced.

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EAAI Journal 2024 Journal Article

Remaining useful life prediction across machines using multi-source adversarial online knowledge distillation

Keying Liu
Yifan Li

Deep transfer learning has been extensively developed in the remaining useful life prediction of rolling bearings because it can decrease the dependence on massive labeled data and robustly extract adaptive features. Most existing studies perform remaining useful life predictions across different working conditions within the same machine. Therefore, these methods are unsuitable for data with severe distribution drifts. Only a few studies investigate the remaining useful life prediction across different devices, but their success depends on the prerequisite that a large amount of labeled data exists. In brief, the models' applicabilities and prediction performances are significantly constrained toward industrial scenarios. To address these drawbacks, a multi-source adversarial online knowledge distillation approach is proposed for rolling bearing remaining useful life prediction across machines. A dual knowledge transfer mechanism, including multi-level domain adaptation and online knowledge distillation with dynamic weighting, is designed in the ensemble learning architecture. The proposed approach allows the more comprehensive extraction and transition of prognostic knowledge of multiple working conditions across different machines, thereby improving the performance of remaining useful life prediction. Experiments on two open-access rolling bearing datasets demonstrate the effectiveness and superiority of the proposed approach in terms of prediction accuracy.

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TMLR Journal 2024 Journal Article

Repositioning the Subject within Image

Yikai Wang
Chenjie Cao
Ke Fan
Qiaole Dong
Yifan Li
Xiangyang Xue
Yanwei Fu

Current image manipulation primarily centers on static manipulation, such as replacing specific regions within an image or altering its overall style. In this paper, we introduce an innovative dynamic manipulation task, subject repositioning. This task involves relocating a user-specified subject to a desired position while preserving the image's fidelity. Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task. Consequently, we can employ a single diffusion generative model to address these sub-tasks using various task prompts learned through our proposed task inversion technique. Additionally, we integrate pre-processing and post-processing techniques to further enhance the quality of subject repositioning. These elements together form our SEgment-gEnerate-and-bLEnd (SEELE) framework. To assess SEELE's effectiveness in subject repositioning, we assemble a real-world subject repositioning dataset called ReS. Results of SEELE on ReS demonstrate its efficacy. Code and ReS dataset are available at https://yikai-wang.github.io/seele/.

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AAAI Conference 2024 Conference Paper

SEIT: Structural Enhancement for Unsupervised Image Translation in Frequency Domain

Zhifeng Zhu
Yaochen Li
Yifan Li
Jinhuo Yang
Peijun Chen
Yuehu Liu

For the task of unsupervised image translation, transforming the image style while preserving its original structure remains challenging. In this paper, we propose an unsupervised image translation method with structural enhancement in frequency domain named SEIT. Specifically, a frequency dynamic adaptive (FDA) module is designed for image style transformation that can well transfer the image style while maintaining its overall structure by decoupling the image content and style in frequency domain. Moreover, a wavelet-based structure enhancement (WSE) module is proposed to improve the intermediate translation results by matching the high-frequency information, thus enriching the structural details. Furthermore, a multi-scale network architecture is designed to extract the domain-specific information using image-independent encoders for both the source and target domains. The extensive experimental results well demonstrate the effectiveness of the proposed method.

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ICRA Conference 2024 Conference Paper

TacShade: A New 3D-printed Soft Optical Tactile Sensor Based on Light, Shadow and Greyscale for Shape Reconstruction

Zhenyu Lu 0001
Jialong Yang
Haoran Li 0013
Yifan Li
Weiyong Si
Nathan F. Lepora
Chenguang Yang 0001

In this paper, we present the TacShade: a newly designed 3D-printed soft optical tactile sensor. The sensor is developed for shape reconstruction under the inspiration of sketch drawing that uses the density of sketch lines to draw light and shadow, resulting in the creation of a 3D-view effect. TacShade, building upon the strengths of the TacTip, a single-camera tactile sensor of large in-depth deformation and being sensitive to edge and surface following, improves the structure in that the markers are distributed within the gap of papillae pins. Variations in light, dark and grey effects can be generated inside the sensor under the external contact interactions. The contours of the contacting objects are outlined by white markers, while the contact depth characteristics can be indirectly obtained from the distribution of black pins and white markers, creating a 2. 5D visualization. Based on the imaging effect, we improve the Shape from Shading (SFS) algorithm to process tactile images, enabling a coarse but fast reconstruction for the contact objects. Two experiments are performed. The first verifies TacShade’s ability to reconstruct the shape of the contact objects through one image for object distinction. The second experiment shows the shape reconstruction capability of TacShade for a large panel with ridged patterns based on the location of robots and image splicing technology.

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AAAI Conference 2024 Conference Paper

Temporal Adaptive RGBT Tracking with Modality Prompt

Hongyu Wang
Xiaotao Liu
Yifan Li
Meng Sun
Dian Yuan
Jing Liu

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

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NeurIPS Conference 2024 Conference Paper

Unified Lexical Representation for Interpretable Visual-Language Alignment

Yifan Li
Yikai Wang
Yanwei Fu
Dongyu Ru
Zheng Zhang
Tong He

Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clarity in its representation and similarity scores. On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation and interpretable, providing exact matches for individual words. However, lexical representations are difficult to learn due to no ground-truth supervision and false-discovery issues, and thus requires complex design to train effectively. In this paper, we introduce LexVLA, a more interpretable VLA framework by learning a unified lexical representation for both modalities without complex design. We use DINOv2 as our visual model for its local-inclined features and Llama 2, a generative language model, to leverage its in-context lexical prediction ability. To avoid the false discovery, we propose an overuse penalty to refrain the lexical representation from falsely frequently activating meaningless words. We demonstrate that these two pre-trained uni-modal models can be well-aligned by fine-tuning on the modest multi-modal dataset and avoid intricate training configurations. On cross-modal retrieval benchmarks, LexVLA, trained on the CC-12M multi-modal dataset, outperforms baselines fine-tuned on larger datasets (e. g. , YFCC15M) and those trained from scratch on even bigger datasets (e. g. , 1. 1B data, including CC-12M). We conduct extensive experiments to analyze LexVLA. Codes are available at https: //github. com/Clementine24/LexVLA.

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IJCAI Conference 2023 Conference Paper

Diffusion Models for Non-autoregressive Text Generation: A Survey

Yifan Li
Kun Zhou
Wayne Xin Zhao
Ji-Rong Wen

Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice the generation accuracy. Recently, diffusion models, a class of latent variable generative models, have been introduced into NAR text generation, showing an improved text generation quality. In this survey, we review the recent progress in diffusion models for NAR text generation. As the background, we first present the general definition of diffusion models and the text diffusion models, and then discuss their merits for NAR generation. As the core content, we further introduce two mainstream diffusion models in existing work of text diffusion, and review the key designs of the diffusion process. Moreover, we discuss the utilization of pre-trained language models (PLMs) for text diffusion models and introduce optimization techniques for text data. Finally, we discuss several promising directions and conclude this paper. Our survey aims to provide researchers with a systematic reference of related research on text diffusion models for NAR generation. We also demonstrate our collection of text diffusion models at https: //github. com/RUCAIBox/Awesome-Text-Diffusion-Models.

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EAAI Journal 2023 Journal Article

Ink painting style transfer using asymmetric cycle-consistent GAN

Weining Wang
Yifan Li
Huan Ye
Fenghua Ye
Xiangmin Xu

Chinese ink painting, an artistic and cultural treasure, necessitates automatic generation for its preservation and evolution. We've innovatively observed and validated that the domain information between photographs and ink paintings, is asymmetrical, which has been overlooked by current style transfer algorithms. We propose an innovative generative adversarial network featuring an asymmetric cyclic consistency structure to address this in ink painting style transfer. This structure uses generators of differing capabilities to align with the asymmetry in transformation directions, improving image quality and model optimization speed. Additionally, we introduce two unique loss functions within the network. The salient edge loss intensifies the subject in the real photo and enhances the edge stroke of the drawn subject, a distinct attribute of ink painting. The feature-wise cycle consistency loss is designed to speed up model optimization. We've also built a Chinese bird ink painting dataset to validate effectiveness of the model. Extensive experiments on this and a public dataset demonstrate that our algorithm can comprehensively learn various stylistic features of ink painting, especially regarding brushstroke style, ink diffusion, and detail preservation. Furthermore, the quantitative results indicate our approach achieves superior results in generation quality and model efficiency compared to existing methods. For instance, compared to the most recent style transfer method, our method achieves an average decrease of 9. 44% and 25. 32% for FID and KID metrics across three datasets and reduces training and inference time costs by 46. 81% and 22. 71% respectively.

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EAAI Journal 2023 Journal Article

Optimal trajectory-tracking guidance for reusable launch vehicle based on adaptive dynamic programming

Xueyun Wang
Yifan Li
Zhiyuan Quan
Jiabao Wu

An optimal trajectory-tracking guidance method for Reusable Launch Vehicle (RLV) based on neural Adaptive Dynamic Programming (ADP) is proposed. Firstly, a reentry reference trajectory and the corresponding steady-state control are generated based on the Gauss Pseudo-spectral Method (GPM) with saturation constraints on the amplitude, rate and acceleration of flow angles. A single-critic ADP controller is designed for optimal feedback control, which is combined with the steady-state control to realize the trajectory-tracking guidance. An innovative weight iteration algorithm for the critic neural network is proposed to reduce training computation and improve guidance accuracy. Simulation results show that with initial state errors and system uncertainty, the terminal position error is within 2. 1 km in a reentry flight whose range covers more than 5500 km. Involved with the innovative weight iteration method, the training computation is reduced by 95% compared with the traditional gradient descent method and the guidance performance is also improved.

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JBHI Journal 2019 Journal Article

Sliding Trend Fuzzy Approximate Entropy as a Novel Descriptor of Heart Rate Variability in Obstructive Sleep Apnea

Yifan Li
Weifeng Pan
Kunyang Li
Qing Jiang
Guanzheng Liu

Obstructive sleep apnea (OSA) is a common sleep disorder that is often associated with reduced heart rate variability (HRV), thus reflecting modulation of the autonomic system. Sliding trend fuzzy approximate entropy (SlTr-fApEn), which is based on the empirical mode decomposition (EMD) method, has been proposed as a novel index for analyzing HRV with OSA. This study included 60 electrocardiogram recordings from the PhysioNet database (40 OSA recordings and 20 healthy recordings) with apnea or no apnea in 5-minute segments. HRV indices obtained by sliding trend analysis were compared to those obtained by time-frequency domain analysis. Among all indices, the ratio of low-frequency power and high-frequency power (LF/HF) and sliding trend indices could significantly differentiate OSA recordings from normal recordings (p <; 0. 05). The OSA screening accuracy of SlTr-fApEn (85%) was higher than that of LF/HF (80%). Disease state analysis showed significant differences in SlTr-fApEn among the control group, normal OSA group, and apnea OSA group (p <; 0. 05). Therefore, SlTr-fApEn can reflect the complexity of autonomic changes during a short time period.

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