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2023 International Conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2023

作者： Xia, Liyang Liu, Kewen Liu, Xinjie Bao, Qingjia Liu, Chaoyang School of Information Engineering Wuhan University of Technology Wuhan China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Centre for Magnetic Resonance Innovation Academy for Precision Measurement Science and Technology Wuhan China

ISBN: (纸本)9781510666238

SPatiotemporal ENcoding (SPEN) has good robustness to inhomogeneous magnetic fields and chemical shifts. However, the eddy current and gradient delay in the acquisition process may cause a certain phase difference between the odd and even line data in the phase encoding dimension, resulting in severe ghost in the reconstructed images. This work proposes a deep learning model for correcting ghost in single-shot SPEN. Experimental results show that the proposed model can effectively correct ghost. © 2023 SPIE.

关键词： Signal encoding

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Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot

Robust Adaptive Filter for Time-Varying Parameters Estimatio...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Zhang, Zhaoyu Duan, Haibin Beihang University School of Automation Science and Electrical Engineering Beijing100083 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beijing100083 China Peng Cheng Laboratory Virtual Reality Fundamental Research Laboratory Department of Mathematics and Theories Shenzhen518000 China

ISBN: (纸本)9798350325706

This paper focuses on the autonomous navigation problem of the fixed-wing aerial robot, of which the inertial measurement unit (IMU) has time-varying error characteristic parameters. A robust variational Bayesian adaptive Kalman filter (RVBAKF) is proposed to tackle the noise outliers in integrated navigation brought by sensor uncertainties. The proposed method formulates the joint probability distribution of the system state vector and the measurement noise covariance matrix. A fading factor matrix with strong tracking quality is introduced to enhance the prediction on the prior distribution of the process noise covariance. Then a weighted sliding window mechanism has been constructed to obtain the posterior distribution of the measurement noise outliers. Therefore, the proposed approach is impressive in approximating both the process and measurement noise. The RVBAKF algorithm is implemented in an integrated navigation system which is composed of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS). The integration framework based on RVBAKF is conducted on two typical verification scenarios, which is proven to be exceptional in coping with the time-varying process and measurement noise by comparing the average root mean square error in misalignment angle, velocity and position with the strong tracking filter and the variational Bayesian filter. © 2023 IEEE.

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A Point–Set–Domain Image Object Matching Method for Airborne Object Localization

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Journal of Internet technology 2025年第3期26卷 303-314页

作者： Liu, Xiaomin Zhao, Runqi Li, Jun-Bao Pan, Jeng-Shyang Zhao, Huaqi The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence and Information Processing Jiamusi University China Faculty of Computing Harbin Institute of Technology China College of Computer Science and Engineering Shandong University of Science and Technology China

Image object localization is an important research direction in the development of intelligent autonomous control systems for unmanned aerial vehicles (UAVs). Major challenges remain, such as cross-view images, large-scale deformation, and multitemporal variation. We propose a point–set–domain matching method to locate objects. First, the property constraints of a point, including sparsity, repeatability, and distinguishability, are combined into a keypoint response used to optimize convolutional neural networks, creating keypoint detector and feature descriptor models. With these models, we can improve the performance of point matching and obtain the corresponding keypoint set accurately. This approach solves the cross-view problem. Second, a spatial transformation model of the corresponding keypoint set is obtained using keypoint-constrained diffeomorphism matching, which can align the spatial location of two images and solve the large-scale deformation problem. Third, an approach combining probability statistics with watershed maximally stable extremal regions is proposed to divide the object image and reference image into several subregions, and then the similarity based on diffeomorphism is employed to localize the object in the UAV image, which solves the multitemporal variation problem. The experimental results show that the proposed method can successfully determine the location of the object in the UAV image. © 2025 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Drones

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Leader-Follower Estimation and Cooperative Guidance with Incomplete information 1

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Wang, Weilin Dong, Wei Deng, Fang Wang, Chunyan Wang, Jianan Guo, Xiaoming Chen, Anhong School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Science and Technology on Space Physics Laboratory Beijing100076 China

ISBN: (数字)9789819733408

ISBN: (纸本)9789819733392

This paper investigates the cooperative estimation and guidance problem to realize the simultaneous attack with incomplete information. First, the leader-follower guidance strategy with unilateral communication and bearing-only measurements is constructed for better survivability and lower cost. Then, a leader-follower estimation method based on the extended Kalman filter (EKF) is put forward for followers to obtain guidance information for impact-time consensus. Furthermore, guidance laws for both the leader and followers are presented to realize the simultaneous attack with impact angle constraints. Finally, numerical simulations are provided to show the effectiveness of the proposed results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extended Kalman filters

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Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances

arXiv

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arXiv 2024年

作者： Zhang, Hanlei Xu, Hua Long, Fei Wang, Xin Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Hebei University of Science and Technology China Technology Development Co. Ltd Nanchang330036 China

Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-K parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://***/thuiar/UMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Fine-Grained Anomaly Detection on Dynamic Graphs via Attention Alignment

Fine-Grained Anomaly Detection on Dynamic Graphs via Attenti...

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International Conference on Data engineering

作者： Dong Chen Xiang Zhao Weidong Xiao National Key Laboratory of Information Systems Engineering National University of Defense Technology China Laboratory for Big Data and Decision National University of Defense Technology China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks imposed by malicious activities. However, identifying anomalies in these dynamic graphs presents a challenging task due to the complex graph structures. Existing methods for dynamic anomaly detection primarily focus on learning representations for each timestamp and using sequence modeling techniques to capture and model the temporal information. Despite extensive research, dynamic anomaly detection still faces two key challenges. First, existing methods are limited in effectively using fine-grained temporal information. Second, they have limited generalization capabilities under unsupervised settings. Overcoming these challenges is crucial for advances in dynamic anomaly detection. In this paper, we propose a novel unsupervised anomaly detection method for dynamic graphs. Our approach leverages complex temporal information through fine-grained sampling and embedding modules. Additionally, we introduce an attention alignment strategy to minimize discrepancies in contextual attention between source and target nodes. Through a comprehensive evaluation, we demonstrate that our strategy effectively mitigates overfitting and improves generalization. Experiments on ten dynamic graph datasets validate the effectiveness of our proposed method in detecting anomalies.

关键词： Image edge detection Contrastive learning Benchmark testing Transformers Data engineering Task analysis Anomaly detection

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Towards Practical Facial Video-based Remote Heart Rate Estimation via Cross Domain rPPG Adaptation 23

Towards Practical Facial Video-based Remote Heart Rate Estim...

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Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics engineering

作者： Ze Yang Haofei Wang Feng Lu Qinping Zhao the State Key Laboratory of Virtual Reality Technology and Systems and the School of Computer Science and Engineering at Beihang University China Peng Cheng Laboratory China the State Key Laboratory of Virtual Reality Technology and Systems and the School of Computer Science and Engineering at Beihang University China and Peng Cheng Laboratory China

ISBN: (纸本)9798400708343

Remote photoplethysmography (rPPG) is a non-contact technology that can estimate heart rate using facial video and holds significant potential for health monitoring. Despite the latest deep learning-based rPPG approaches can predict high-quality rPPG signal under similar scenarios, these methods often suffer from degraded performance when encountering variations in subjects, environments, or illumination conditions in target domains. To address this challenge, we propose an uncertainty-guided self-training approach that leverages model uncertainty and periodic priors to enhance generalization across different domains without requiring labels in the target domain. We iteratively update the model using pseudo-labels generated from its own predictions on unlabelled data in the target domain, with varying confidence levels informed by the model’s uncertainty estimation. To achieve this, we modify a standard Convolutional Neural Network (CNN) into a Bayesian Neural Network (BNN) for uncertainty estimation, which guides the assignment of pseudo-labels with varying confidence levels. By employing the adversarially learned periodic priors of rPPG signals shared across domains as regularization terms, we further stabilize the model adaptation process. We evaluate the proposed method on two public datasets (PURE and UBFC-rPPG) across five cross-domain tasks. Experimental results demonstrate improved performance over the baselines, with gains ranging from 60.5% to 97.2%, outperforming existing methods in generalization performance for rPPG-based heart rate measurement.

关键词： heart rate estimation remote photoplethysmography unsupervised domain adaptation

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Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

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Chinese Journal of Aeronautics 2021年第10期34卷 166-176页

作者： Hao ZHANG Zongxia JIAO Yaoxing SHANG Xiaochao LIU Pengyuan QI Shuai WU School of Automation Science and Electrical Engineering Bcihcmg UniversityBeijing 100083China Research Institute for Frontier Science Beihang UniversityBeijing 1000H3China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Key Laboratory of Advanced Aircraft Systems(Beihang University) Ministry of Industry and Information TechnologyBeijing 100083China Ningbo Institute of Technology Beihang UniversityNingbo 315800China The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University.Hangzhou 310027China

The maneuvering time on the ground accounts for 10%–30%of their flight time,and it always exceeds 50%for short-haul aircraft when the ground traffic is *** also contribute significantly to emissions,fuel burn,and noise when taxiing on the ground at *** is an urgent need to reduce aircraft taxiing time on the ***,it is too expensive for airports and aircraft carriers to build and maintain more runways,and it is space-limited to tow the aircraft fast using *** drive capability is currently the best solution for aircraft,which can save the maneuver time for *** idea is proposed that the wheels are driven by APU-powered(auxiliary power unit)motors,APU is working on its efficient point;consequently,the emissions,fuel burn,and noise will be reduced *** Front-wheel drive aircraft,the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a *** traction effects the aircraft’s maximum turning ability,which is difficult to be modeled to guide the controller *** reinforcement learning provides a powerful tool to help us design controllers for black-box models;however,the models of related works are always simplified,fixed,or not easily modified,but that is what we care about *** with complex models can the trained controller be ***-fidelity models that can easily modified are necessary for aircraft ground maneuver controller *** paper focuses on the maneuvering problem of front-wheel drive aircraft,a high-fidelity aircraft taxiing dynamic model is established,including the 6-DOF airframe,landing gears,and nonlinear tire force model.A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive *** is proved that in some conditions,the DRL based controller outperformed conventional look-ahead controllers.

关键词： Front-wheel drive aircraft Ground maneuver Nose wheel steering Tire model Magic equation Deep reinforcement learning

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Efficiently band-tailored type-Ⅲ van der Waals heterostructure for tunnel diodes and optoelectronic devices

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Nano Research 2022年第9期15卷 8442-8450页

作者： Xiangna Cong Yue Zheng Fu Huang Qi You Jian Tang Feier Fang Ke Jiang Cheng Han Yumeng Shi International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale OptoelectronicsShenzhen UniversityShenzhen 518060China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics&Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China

Broken-gap(type-Ⅲ)two-dimensional(2D)van der Waals heterostructures(vdWHs)offer an ideal platform for interband tunneling devices due to their broken-gap band offset and sharp band ***,we demonstrate an efficient control of energy band alignment in a typical type-ⅢvdWH,which is composed of vertically-stacked molybdenum telluride(MoTe2)and tin diselenide(SnSe2),via both electrostatic and optical *** a single electrostatic gating with hexagonal boron nitride(hBN)as the dielectric,a variety of electrical transport characteristics including forward rectifying,Zener tunneling,and backward rectifying are realized on the same heterojunction at low gate voltages of±1 *** particular,the heterostructure can function as an Esaki tunnel diode with a room-temperature negative differential *** great tunability originates from the atomicallyflat and inert surface of h-BN that significantly suppresses the interfacial trap scattering and strain *** the illumination of an 885 nm laser,the band alignment of heterojunction can be further tuned to facilitate the direct tunneling of photogenerated charge carriers,which leads to a high photocurrent on/off ratio of>105 and a competitive photodetectivity of 1.03×1012 Jones at zero ***,the open-circuit voltage of irradiated heterojunction can be switched from positive to negative at opposite gate voltages,revealing a transition from accumulation mode to depletion *** findings not only promise a simple strategy to tailor the bands of type-ⅢvdWHs but also provide an in-depth understanding of interlayer tunneling for future low-power electronic and optoelectronic applications.

关键词： broken-gap(type-Ⅲ)heterostructure band tailoring single electrostatic gating infrared photodetector photovoltaic effects

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