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3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Wu, Haobo Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict Detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice. © 2024 IEEE.

关键词： Spheres

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Liver Tumor image Segmentation Method Based on U-Net Network 1

Liver Tumor Image Segmentation Method Based on U-Net Network

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Lu, Dinghong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

Liver tumors are the most common and fatal ones globally, bringing great harm to human life. In order to ascertain the precise location, size, and volume of tumors, the conventional method of manual segmentation necessitates that physicians delineate the tumor boundaries within the patients' abdominal CT scans, a process that demands considerable expenditure of time and effort on the part of the medical staff. A method harnessing the U-Net neural network, predicated on deep learning, has been introduced in this study to augment the accuracy and efficiency of liver tumor segmentation in imaging. The U-Net network has been enhanced with the incorporation of Adam optimization and callback mechanisms to bolster the training efficacy and preserve the most effective model. Furthermore, the implementation of the binary crossentropy loss function has been pivotal for optimizing the neural network's convergence and predictive accuracy, which in turn has led to enhanced segmentation results. The experimental findings suggest that the proposed method is capable of automating the segmentation of liver tumor images, including the precise delineation of tumors and those with indistinct margins, achieving a segmentation accuracy of 99.64%. © 2024 IEEE.

关键词： Computerized tomography

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Enhancing Deep Learning-based Depression Level Estimation Based on Multi-task Learning 24

Enhancing Deep Learning-based Depression Level Estimation Ba...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Ye, Qingxin Xie, Zhenming Sun, Hao Xin, Luyao Chen, Youwen Song, Jian Chen, Yen-Wei Provincial key Laboratory of Computational Science Huaqiao University Quanzhou China Intelligent Image Processing Lab Zhejiang University Hangzhou China Intelligent Image Processing Lab Ritsumeikan University Osaka Japan

ISBN: (纸本)9798400717833

In recent years, depression, as a serious mental illness, has received widespread attention from various sectors of society. How to identify depressive emotions in a timely manner and detect depression has become an urgent issue that needs to be addressed. Currently, many experts and scholars propose using deep learning methods to assess the level of depression. Compared to other deep learning models, Transformer models have parallel computing capabilities with their self-attention mechanism, enabling them to handle entire sequences and capture long-distance dependencies. After the success of Transformers in natural language processing, similar architectures have been introduced into the field of computer vision, promoting advancements in multi-modal research. However, how to integrate other information or tasks to improve assessment accuracy remains a challenge. In our team's preliminary work, we proposed a multi-modal adaptive fusion Transformer network for assessing depression levels. Inspired by multi-task learning, we introduced gender classification as a second auxiliary task alongside the main task of depression regression, forming a three-task learning framework including depression classification. This was done to enhance the model's performance. We validated the effectiveness of the proposed method by testing it on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge). The experimental results show that our proposed method achieved a Concordance Correlation Coefficient (CCC) of 0.60448, which is 1.688% higher than the accuracy of the dual-task learning method (CCC = 0.5876). © 2024 Copyright held by the owner/author(s).

关键词： Multi-task learning

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Applications of artificial intelligence to lipid nanoparticle delivery

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Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

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AN ADVERSARIAL AND DEEP HASHING-BASED HIERARCHICAL SUPERVISED CROSS-MODAL image AND TEXT RETRIEVAL ALGORITHM

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International Journal of Robotics and Automation 2024年第1期39卷 77-86页

作者： Chen, Ruidong Qiang, Baohua Zhou, Mingliang Zhang, Shihao Zheng, Hong Tang, Chenghua Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science Chongqing University Chongqing400044 China

With the rapid development of robotics and sensor technology, vast amounts of valuable multimodal data are collected. It is extremely critical for a variety of robots performing automated tasks to find relevant multimodal information quickly and efficiently in large amounts of data. In this paper, we propose an adversarial and deep hashing-based hierarchical supervised cross-modal image and text retrieval algorithm to perform semantic analysis and association modelling on image and text by making full use of the rich semantic information of the label hierarchy. First, the modal adversarial block and the modal differentiation network both perform adversarial learning to keep different modalities with the same semantics closest to each other in a common subspace. Second, the intra-label layer similarity loss and inter-label layer correlation loss are used to fully exploit the intrinsic similarity existing in each label layer and the correlation existing between label layers. Finally, an objective function for different semantic data is redesigned to keep data with different semantics away from each other in a common subspace, thus avoiding interference of retrieval by data of different semantics. The experimental results on two cross-modal retrieval datasets with hierarchically supervised information show that the proposed method substantially enhances retrieval performance and consistently outperforms other state-of-the-art methods. © 2024 Acta Press. All rights reserved.

关键词： Semantics

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Few-Shot Learning for image Classification 1

Few-Shot Learning for Image Classification

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Chen, Peining Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intellig ent Processing Guilin China

ISBN: (纸本)9798331504434

In certain specialized domain scenarios, the collection of image data is rendered time-consuming and costly due to a variety of unique issues, making the assembly of extensive image datasets a challenging endeavor. When compared to the thousands, if not tens of thousands of image data required for deep learning models, the amount of image data that can be procured is significantly dwarfed. However, few-shot learning facilitates the training of models with a limited number of labeled images, eliminating the need for extensive data resources. In this paper, image enhancement activated to enrich the finite image dataset, thereby diversifying the data and highlighting key features through the improvement of visual effects and machine interpretability. Additionally, the method of transfer learning is engaged to optimize the classification performance of deep learning models. The selection of the ResNet-50 architecture serves as the foundational framework for the network. Prior knowledge is obtained from the imageNet dataset. A strategy that combines image enhancement and transfer learning, where the original data is processed using image enhancement to improve the convergence rate of the loss function and the model's classification accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Effect of aggregation on thermally activated delayed fluorescence and ultralong organic phosphorescence:QM/MM study

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Chinese Physics B 2023年第10期32卷 410-419页

作者：张群王晓菲吴智敏李小芳张凯宋玉志范建忠王传奎蔺丽丽 Shandong Key Laboratory of Medical Physics and Image Processing&Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and ElectronicsShandong Normal UniversityJinan 250358China

Aggregation-induced thermally activated delayed fluorescence(TADF)phenomena have attracted extensive attention *** this paper,several theoretical models including monomer,dimer,and complex are used for the explanation of the luminescent properties of(R)-5-(9H-carbazol-9-yl)-2-(1,2,3,4-tetrahydronaphthalen-1-yl)isoindoline-1,3-dione((R)-ImNCz),which was recently reported[Chemical Engineering Journal 418129167(2021)].The polarizable continuum model(PCM)and the combined quantum mechanics and molecular mechanics(QM/MM)method are adopted in simulation of the property of the molecule in the gas phase,solvated in acetonitrile and in aggregation *** is found that large spin–orbit coupling(SOC)constants and a smaller energy gap between the first singlet excited state and the first triplet excited state(△E_(st))in prism-like single crystals(SC_(p)-form)are responsible for the TADF of(R)-lmNCz,while no TADF is found in block-like single crystals(SC_(b)-form)with a larger △E_(st).The multiple ultralong phosphorescence(UOP)peaks in the spectrum are of complex origins,and they are related not only to ImNCz but also to a minor amount of impurities(ImNBd)in the crystal prepared in the *** dimer has similar phosphorescence emission wavelengths to the(R)-lmNCz-SC_(p) *** complex composed of(R)-lmNCz and(R)-lmNBd contributes to the phosphorescent emission peak at about 600 nm,and the phosphorescent emission peak at about 650 nm is generated by(R)-*** indicates that the impurity could also contribute to emission in molecular *** present calculations clarify the relationship between the molecular aggregation and the light-emitting properties of the TADF emitters and will therefore be helpful for the design of potentially more useful TADF emitters.

关键词： organic light-emitting diodes thermally activated delayed fluorescence ultralong organic phosphorescence aggregation mode

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Distributed robust iterative learning control for AC microgrids with external disturbances

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Science China(Information Sciences) 2025年

作者： Hongyu SU Jingang LAI School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology

This study proposes a distributed secondary control scheme based on distributed robust iterative learning control(DRILC) for islanded alternating current(AC) microgrids subjected to external disturbances. By employing the decoupled sliding mode consensus approach, voltage regulation, frequency restoration, and accurate active power sharing can be achieved within a finite time on the proposed novel integral terminal sliding mode(ITSM) manifold. Furthermore, the appropriate iterative update law in the ITSM-based controller is utilized to learn and eliminate external disturbances as effectively as possible. In the proposed control scheme, the iterative learning control and sliding mode control are designed to function in a complementary manner, which enhances the performance of the secondary control scheme for multi-objective regulations. The stability criteria and robustness to external disturbances of the closed-loop microgrid system in the iteration and time domains are also rigorously derived with the help of the Lyapunov direct method. Finally, the effectiveness of the proposed secondary control scheme is validated by case studies of an islanded AC microgrid test system in the MATLAB/SimPowerSystems software environment.

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval 2

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Li, Fang Dai, Liulian Lan, Rushi Yang, Rui Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9798331515669

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences"and "semantic gap"within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval"and "text-to-image retrieval"tasks. © 2024 IEEE.

关键词： Semantics

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