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42nd Chinese Control Conference, CCC 2023

作者： Siting, Peng Lei, Li Zhu, Hongbo Jun, Ren Zhichao, Song Yang, Jiabing The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

ISBN: (纸本)9789887581543

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feature extraction

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

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International Journal of Digital Earth 2023年第1期16卷 272-288页

作者： Chunmei He Jia Sun Yuwen Chen Lunche Wang Shuo Shi Feng Qiu Shaoqiang Wang Torbern Tagesson Hubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China Department of Physical Geography and Ecosystem Science Lund UniversityLundSweden State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China International Institute for Earth System Science Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingPeople’s Republic of China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to ***,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their *** study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and *** sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT *** VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent *** ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl *** with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield *** new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

关键词： Leaf carotenoids content leaf chlorophyll content PROSPECT model ratio of Car to Chl vegetation index

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer

Deep Learning and Machine Learning Algorithms to Predict Lun...

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Emerging Trends in information technology and engineering (ic-ETITE), Conference on

作者： K. Gowthami Kamalakannan. J School of Computer Science Engineering and Information System. Vellore Institute Of Technology Vellore India

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of Computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM).

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Cooperative Relationship Prediction between Scholars in Heterogeneous Academic Network 23

Cooperative Relationship Prediction between Scholars in Hete...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shi, Jia Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

The real academic network belongs to a heterogeneous network, therefore, for the link prediction tasks, some information on the network may be lost if only using homogeneous network methods. In order to make good use of the rich semantic information in a heterogeneous network for better predicting the cooperative relationship between scholars, this paper proposes a cooperative relationship prediction (CoRP) model. The CoRP model includes the following three steps. Firstly, a structural feature extraction module is designed based on meta path by using the similarity metric function to measure the meta path similarity between scholars. Secondly, to overcome the defects that the existing methods usually ignore the attribute information on nodes, three attribute feature presentation functions are constructed to fully utilize the attribute information of scholars in the academic network. In the end, a machine learning classifier which combines the structural similarity and attribute similarity is utilized to learn each attribute feature and complete the task of cooperative relationship prediction. The experimental results reveal that compared with the benchmark method, the proposed method has better performance and interpretability with improvement from 3% to 10% of AUC value for the AMiner academic data set. © 2021 IEEE.

关键词： Measurement Smart cities Semantics Machine learning Benchmark testing Predictive models Feature extraction

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Privacy-Preserving Federated Primal - Dual Learning for Nonconvex and Nonsmooth Problems With Model Sparsification

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IEEE Internet of Things Journal 2024年第15期11卷 25853-25866页

作者： Li, Yiwei Huang, Chien-Wei Wang, Shuai Chi, Chong-Yung Quek, Tony Q. S. Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Singapore University of Technology and Design Information Systems Technology and Design Tampines487372 Singapore Yonsei University Yonsei Frontier Lab Seoul03722 Korea Republic of

Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data. This article delves into a class of federated problems characterized by nonconvex and nonsmooth (NCNS) loss functions, that are prevalent in FL applications but challenging to handle due to their intricate nonconvexity and nonsmoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this article, we propose a novel federated primal - dual algorithm with bidirectional model sparsification tailored for NCNS FL problems, and differential privacy is applied for privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented as the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties. © 2014 IEEE.

关键词： Learning systems

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Analysis and Research on BOX64's Core Technologies for Cross-Architecture Software Migration

Analysis and Research on BOX64's Core Technologies for Cross...

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2023 International Conference on Frontiers of Robotics and Software engineering, FRSE 2023

作者： Zhang, Jin Shan, Zehu Liu, Xiaodong Wang, Wenzhu Li, Zhuoheng Peng, Long Yu, Jie Hunan Normal University College of Computer and Communication Engineering Changsha University of Science and Technology College of Information Science and Engineering Changsha China Hunan Normal University College of Information Science and Engineering Changsha China National University of Defense Technology College of Computer Science Changsha China Haihe Lab of Itai Tianjin China Kylin Software Tianjin China

ISBN: (纸本)9798350301113

In the current context of increasingly diverse processor architectures and complex application requirements, how to seamlessly migrate software written for specific architectures (such as X86) to other architectures (such as ARM) has become a critical and urgent issue to resolve. This research primarily explores the key roles of WRAPPER and Dynarec technologies in this process. WRAPPER technology encapsulates the library function calls and system calls of X86_64 Linux programs, enabling them to run seamlessly and efficiently on the ARM64 Linux platform, thereby achieving cross-architecture software compatibility. Dynamic recompilation technology, on the other hand, can dynamically convert the binary code of the source architecture into the binary code of the target architecture during runtime, further enhancing program performance. Through in-depth theoretical analysis and empirical testing, the research team has found that there is still potential for optimization in these two technologies regarding library function calls and memory management. The team conducted a comprehensive theoretical analysis and evaluation of possible optimization strategies, expecting to further improve software performance on non-X86_64 architecture platforms through the implementation of these strategies. This has practical research value for the development and porting of software on non-X86_64 architecture platforms. © 2023 IEEE.

关键词： Binary codes

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An Adaptive Sliding Mode Compound Control Method of Interceptors

An Adaptive Sliding Mode Compound Control Method of Intercep...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Xiong, Jiafu Li, Junlong Zhang, Rui Wang, Chengcai Ran, Maopeng School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Electronic System Engineering Beijing100854 China China Academy of Electronics and Information Technology Beijing100041 China

ISBN: (纸本)9789811966125

An novelty control method utilizing adaptive sliding mode is presented for the interceptor’s autopilot system with interference in this paper. The algorithm employs the ideas of sliding mode control, radial basis neural networks and adaptive control. A fast integral sliding mode surface, defined by the angular error, is first proposed. To enhance the robustness of the closed-loop system, a sliding mode control law with partial parameters updated by neural networks is derived. Furthermore, an allocation algorithm is introduced to establish overload quickly. The strategy can avoid initial full deflection of rudder and improve the dynamic transition process between direct force and aerodynamic force in complex flight environment. Numerical simulation results bear out the availability and superiority of the designed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sliding mode control

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X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation 24

X-Prompt: Multi-modal Visual Prompt for Video Object Segment...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Pinxue Li, Wanyun Huang, Hao Hong, Lingyi Zhou, Xinyu Chen, Zhaoyu Li, Jinglun Jiang, Kaixun Zhang, Wei Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology China

ISBN: (纸本)9798400706868

Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where traditional VOS methods struggle, such as extreme illumination, rapid motion, and background distraction. Existing approaches often involve designing specific additional branches and performing full-parameter fine-tuning for fusion in each task. However, this paradigm not only duplicates research efforts and hardware costs but also risks model collapse with the limited multi-modal annotated data. In this paper, we propose a universal framework named X-Prompt for all multi-modal video object segmentation tasks, designated as RGB+X. The X-Prompt framework first pre-trains a video object segmentation foundation model using RGB data, and then utilize the additional modality of the prompt to adapt it to downstream multi-modal tasks with limited data. Within the X-Prompt framework, we introduce the Multi-modal Visual Prompter (MVP), which allows prompting foundation model with the various modalities to segment objects precisely. We further propose the Multi-modal Adaptation Experts (MAEs) to adapt the foundation model with pluggable modality-specific knowledge without compromising the generalization capacity. To evaluate the effectiveness of the X-Prompt framework, we conduct extensive experiments on 3 tasks across 4 benchmarks. The proposed universal X-Prompt framework consistently outperforms the full fine-tuning paradigm and achieves state-of-the-art performance. Code: https://***/PinxueGuo/*** © 2024 ACM.

关键词： Image segmentation

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Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping in Real Time 42

Tightly-Coupled Lidar-Visual-Inertial Odometry and Mapping i...

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42nd Chinese Control Conference, CCC 2023

作者： Ma, Wenyu Tian, Bailing Lu, Hanchen Shen, Hongming School of Electrical and Information Engineering Tianjin University Tianjin300072 China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789887581543

We proposed a tightly-coupled Lidar-visual-inertial odometry and mapping method, which takes advantage of measurement of Lidar, visual and inertial sensors to achieve highly accurate, real-time 6DoF state estimation and map-building in GNSS-denied environments. The proposed odometry is a tightly-coupled optimization-based method, obtains robust and low drift odometry by fusing pre-integrated IMU measurements, visual features from the image, and geometric features from Lidar data. Further, we adapt an online method to mitigate degeneracy in optimization problems to improve robustness in environmentally degenerate cases. Simulation and real-world experiments show that the proposed method exhibits similar or better robustness and accuracy with the state-of-the-art SLAM methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Optical radar

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