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arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

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Motion Sickness Alleviation Based on a Mindfulness Brain-computer Interface

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jiawei Zhu Xiaoyu Bao Qiyun Huang Tao Wang Li Huang Yupeng Han Kendi Li Di Chen Kailin Xu Zijian Wang Ya Jiang Yuanqing Li Dept. School of Automation Science and Engineering South China University of Technology Dept. Research Center for Brain–Computer Interface Pazhou Laboratory Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China Dept. Research Center for Brain–Computer Interface Pazhou Laboratory South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t 30 =4.968, P<0.001). To the best of our knowledge, our mindfulness BCI is the first effective non- pharmacological alleviation method for motion sickness as demonstrated by a strict test.

关键词： Training Sociology Motion sickness Motors Brain-computer interfaces Automobiles Statistics

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Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Wu, Xiaoxiao Gao, Zhenguo Chen, Xiaowei Wang, Yakai Qu, Shulei Li, Na Department of Computer Science and Technology Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University No.668 Jimei Avenue Fujian Xiamen361021 China Key Laboratory of Computer Vision and Machine Learning Fujian Provincial Universities Fujian Xiamen361021 China

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs highquality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance. © 2024, CC BY.

关键词： Image segmentation

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Text Generation of Speech Imagery Based on an Enhanced CTA-BiLSTM Model Utilizing EEG Signals

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IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Hongguang Chu, Xin Miao, Rui Wang, Mei Wang, Yiran Li, Zhuoyi College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an710054 China Xi’an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security Xi’an710054 China LTD Company of Shenzhen HQVT Technology Co Shenzhen518100 China College of Computer Science and Technology Xi’an University of Science and Technology Xi’an710054 China Northwestern Polytechnic University School of Automation Xi’an710072 China

Recent studies have demonstrated the potential application of speech imagery neural signals in brain–computer interface (BCI) technology. Text generation based on speech imagery offers a natural communication method for individuals with speech disabilities. However, the limitations in imagined content and the immaturity of text generation technology currently constitute an obstacle to its applications. Therefore, this study proposes an enhanced CTA-BiLSTM model for efficient text generation utilizing speech imagery electroencephalography (EEG) signals, significantly enhancing the accuracy and fluency of text generation. Firstly, distinct from the prevailing imagination of characters and words, this study has assembled a sentence-level EEG dataset from ten subjects to facilitate communication. Subsequently, addressing the temporal dynamics characteristics and sequence dependencies of sentence signals, we employ dynamic time warping (DTW) and hidden Markov models (HMM) for accurate temporal alignment and signal annotation to generate fine-grained sentence labels. Finally, the proposed CTA-BiLSTM model leverages channel-time attention mechanism to dynamically adjust weights across channels and time, emphasizing critical features. Concurrently, the bidirectional long short-term memory (BiLSTM) network captures and utilizes long-term dependencies in the EEG signals, thereby enhancing the accuracy of the model in decoding complex temporal patterns. The experimental results demonstrate that the average sentence decoding accuracy can reach 67.50% on the self-built dataset, realizing a better evaluation accuracy and validating its potential for application. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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Advanced AI techniques for root disease classification in dental X-rays using deep learning and metaheuristic approach

Intelligent Systems with Applications

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Intelligent Systems with Applications 2025年 26卷

作者： Enkvetchakul, Prem Khonjun, Surajet Pitakaso, Rapeepan Srichok, Thanatkij Luesak, Peerawat Kaewta, Chutchai Gonwirat, Sarayut Boonmee, Chawis Noowattana, Matus Srisuwandee, Thitinon Department of Information Technology Faculty of Science Buriram Rajabhat University Buriram31000 Thailand Artificial Intelligence Optimization SMART Laboratory Industrial Engineering Department Faculty of Engineering Ubon Ratchathani University Ubon Ratchathani34190 Thailand Department of Industrial Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai57120 Thailand Digital Innovation Faculty of Computer Science Ubon Ratchathani Rajabhat University Mueang Ubon Ratchathani34000 Thailand Department of Computer Engineering and Automation Kalasin University Kalasin46000 Thailand Department of Industrial Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Dental Department Samrong Hospital Samrong District Ubon Ratchathani34360 Thailand

Root dental diseases remain among the most diagnostically challenging conditions in oral healthcare, often leading to treatment delays and suboptimal outcomes. This study is motivated by the limitations of existing automated diagnostic systems, which tend to focus on superficial abnormalities and overlook complex root pathologies such as pulpal infections, periapical lesions, and progressive periodontitis. To bridge this critical gap, we propose an advanced AI-based classification model that integrates ensemble deep learning architectures with a hybrid metaheuristic optimization strategy-namely, the non-population-based Artificial Multiple Intelligence System (np-AMIS) for image augmentation and the population-based AMIS (pop-AMIS) for adaptive decision fusion. This dual-phase approach enhances feature diversity, classification robustness, and computational efficiency. The model was trained and validated on two proprietary datasets, TD-1 and TD-2, achieving classification accuracies of 98.87 % and 98.41 %, respectively. It was further implemented in a real-world application via the Automated Teeth Disease and Abnormality Classification System (A-TD-A-CS), demonstrating 98.95 % accuracy, a rapid response time of 1.5 s, and a System Usability Scale (SUS) score of 94.5 from dental professionals. The system's ability to accurately identify multiple root disease categories highlights its clinical viability and transformative potential. In addition to its current performance, this study lays the groundwork for future extensions to multi-center datasets and cross-modality diagnostics using cone-beam CT or intraoral scans, further advancing intelligent dental care. © 2025

关键词： Deep learning

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Active Disturbance Rejection Intake Pressure Control of Aeropropulsion Systems Test Facility with Adaptive Parameter B0

SSRN

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SSRN 2024年

作者： Xu, Zhuang Zhang, Hehong Zhai, Chao Xiao, Gaoxi Qian, Qiumeng Huang, Feng College of Computer and Data Science Fuzhou University Fuzhou350108 China Wuhan430074 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Science and Technology on Altitude Simulation Laboratory AECC Sichuan Gas Turbine Establishment Mianyang621703 China School of Mechanical Engineering and Automation Fuzhou University Fuzhou350108 China

Establishing a precise intake pressure environment in the aeropropulsion systems test facility (ASTF) is critical for evaluating aeroengines’flight characteristics. The model-partial and robust active disturbance rejection control (ADRC), that is extended stste observer (ESO)-based controller, has become a favorable solution to the complex intake pressure control system. The parameter b0, as the estimated value of the closed-loop system control gain b under the ADRC paradigm, significantly influences ADRC performances, evidenced by extensive flight tests. In this work, an enhanced ADRC with an adaptive parameter b0 is proposed to improve the control quality of the intake pressure. In particular, the simplified model of b is utilized to dynamically adjust b0. To enhance the timeliness of b0, meanwhile, differential signals are obtained by tracking differentiator (TD) to compensate for the adaptive law. The stable region of b0 is theoretically analyzed by using the time delay estimation technique, and the quantitative relationship between b0 and disturbance observation ability of ESO is given. Experimental results demonstrate that the proposed controller can effectively suppress the valve swing amplitude in steady state and reduce the maximum pressure error in transitional state, with improvement ratesof 50% and 61%, respectively. © 2024, The Authors. All rights reserved.

关键词： Disturbance rejection

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Detection Data Characteristics and Recognition of Tunnel Fissure Water

SSRN

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SSRN 2023年

作者： Li, Chuan Wang, Shiyi Wang, Yunsheng Zhang, Panpan Yang, Xi Wan, Xiaorong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Yunnan Aerospace Engineering Geophysical Detecting co.ltd Yunnan Kunming650500 China

Tunnel fissure water may induce geological disasters of mud bursting and collapse during the construction stage. Therefore, it is important to automatically extract and identify fissure water in front of the tunnel face for tunnel construction. The paper performs diffraction migration imaging of seismic data after P/S wave separation, and the imaging results reflect the three-dimensional distribution of undesirable geologic bodies. Second, based on the geologic bodies obtained above, ground-penetrating radar detection is added to identify alternative area of fissure water at the peak point of the A-scan. The boundary point of the alternative area of fissure water is located at the peak point where the phase of the reflected wave and the incident wave are reversed. Finally, the area with a larger amplitude and smaller frequency than the surrounding rock is screened as the target area among the alternative area, and the distribution of fissure water is labeled in the B-scan. The method is applied to the mileage section K38+440$\sim$+320 of Liangwangshan Tunnel in Yunnan, and the forecast results show that the rock bodies near the mileage section K38+440$\sim$+410, K38+410$\sim$+380, and K38+350$\sim$+320 is more fragmented, the fissure water is developed, and the target area is verified by excavation. © 2023, The Authors. All rights reserved.

关键词： Ground penetrating radar systems

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Single image unlearning: efficient machine unlearning in multimodal large language models 24

Single image unlearning: efficient machine unlearning in mul...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiaqi Li Qianshan Wei Chuanyi Zhang Guilin Qi Miaozeng Du Yongrui Chen Sheng Bi Fan Liu School of Cyber Science and Engineering Southeast University Nanjing China and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China College of Artificial Intelligence and Automation Hohai University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China and School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798331314385

Machine unlearning (MU) empowers individuals with the 'right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the leaked visual data of concepts. To overcome the challenge, we propose an efficient method, Single Image Unlearning (SIU), to unlearn the visual recognition of a concept by fine-tuning a single associated image for few steps. SIU consists of two key aspects: (i) Constructing Multifaceted fine-tuning data. We introduce four targets, based on which we construct fine-tuning data for the concepts to be forgotten; (ii) Joint training loss. To synchronously forget the visual recognition of concepts and preserve the utility of MLLMs, we fine-tune MLLMs through a novel Dual Masked KL-divergence Loss combined with Cross Entropy loss. Alongside our method, we establish MMUBench, a new benchmark for MU in MLLMs and introduce a collection of metrics for its evaluation. Experimental results on MMUBench show that SIU completely surpasses the performance of existing methods. Furthermore, we surprisingly find that SIU can avoid invasive membership inference attacks and jailbreak attacks. To the best of our knowledge, we are the first to explore MU in MLLMs. We will release the code and benchmark in the near future.

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Improving GNSS positioning correction using Deep Reinforcement Learning with Adaptive Reward Augmentation Method 36

Improving GNSS positioning correction using Deep Reinforceme...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Tang, Jianhao Li, Zhenni Guo, Rui Zhao, Haoli Wang, Qianming Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9780936406350

High-precision global navigation satellite system (GNSS) positioning for automatic driving in urban environments is an unsolved problem because of the influence of multipath effects. Recently, methods based on data-driven deep reinforcement learning (DRL) have been used to learn continual positioning-correction strategies without strict assumptions about model parameters, and are adaptable to nonstationary urban environments. However, these methods face two remaining challenges: 1) real-time data for training collected in nonstationary urban environments is inadequate because of issues such as response delay and signal interruption, which causes the performance degradation in DRL, 2) the existing methods use vehicle positions as the environment observations, ignoring the complex errors caused by multipath effects in urban environments. In this paper, we propose a novel DRL-based positioning-correction method with an adaptive reward augmentation method (ARAM), and use GNSS measurements instead of vehicle positions as the environment observations, for improving the GNSS positioning accuracy in nonstationary urban environments. To specify the accurate current state of the vehicle agent, we use the GNSS measurement observations, including the line-of-sight (LOS) vector and the pseudorange residual, to model complex environmental errors, and employ a long and short term memory (LSTM) module to learn the temporal aspects of the observations, which include the interference by multipath effects on the GNSS positioning in urban environments. To address the performance degradation caused by inadequate real-time training data, we employ ARAM to adaptively modify the matching of data between the source domain and target domain of the nonstationary urban environments, to leverage sufficient data from the source domain for training, and thus to improve the performance of DRL. Hence, based on ARAM and using the GNSS measurement observations, we construct an LSTM-based proximal pol

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