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Research Square 2021年

作者： Fan, Chaodong Hou, Bo Qu, Xilong Xiao, Leyi Cheng, Fanyong Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha410205 China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui Polytechnic University Wuhu241000 China Foshan Green Intelligent Manufacturing Research Institute Xiangtan University Guangdong 528311 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model;secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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Multidomain Secure Communication and intelligent Traffic Detection Model in VANETs

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International Journal of intelligent Systems 2025年第1期2025卷

作者： Qikun Zhang Mengqi Liu Ping Li Junling Yuan Hongfei Zhu School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450001 China *** School of Cyber Science and Engineering Southeast University Nanjing 210000 China seu.edu.bd School of Information Security Sanmenxia College of Social Administration Sanmenxia 472000 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China ***

Vehicular ad-hoc network (VANET) plays a vital role in the intelligent transportation system. It is crucial to ensure secure communication among entities in the VANET for realizing an efficient transportation system. In this scenario, the current communication scheme is vulnerable to the leakage of private information from entities. The research primarily centers on single-domain vehicular networks, with only a limited number of researchers exploring cross-domain authentication among vehicle entities. Cross-domain communication schemes have received little attention from scholars. Furthermore, there are issues, including the susceptibility of in-vehicle conversations to eavesdropping, the vulnerability of long-distance transmissions to interruptions, and the exposure of wireless networks to traffic attacks. To address these issues, a multidomain secure communication and intelligent traffic detection model in VANET is proposed. This model offers several notable advantages as follows: (1) It employs a key self-verification algorithm for local computation and authentication of entity keys. This approach mitigates the risks of identity impersonation attacks and key leakage results from third-party key escrow. (2) A multidomain communication scheme is devised to categorize vehicle-to-vehicle (V2V) scenarios into intradomain and interdomain, which correspond to situations where the communicating parties are within the same domain and across different domains, respectively. (3) We propose the implementation of new session message encryption algorithms for V2V communication. This involves generating dynamic random keys to ensure secure data sharing and facilitates long-distance cross-domain communication among vehicles. (4) An intelligent two-layer traffic detection paradigm is proposed to improve the efficiency of detecting attack traffic in vehicular networks. This paper provides security proofs and performance analysis of the proposed scheme. The experimental results dem

关键词： Secure communication

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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

arXiv

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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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Particle swarm optimization based on weighted aggregation degree and adaptive decision

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第1期82卷 231-240页

作者： Wan, Renxia Zhu, Lijun Liu, Kai Chen, Ruidian Ningxia Key Laboratory of Intelligent Information and Big Data Processing North Minzu University China College of Mathematics and Information Science North Minzu University China Mathematics’ Group Pinghu College China Fujian Hongyang Software co. LTD China

In response to the problem that particle swarm optimization algorithm (PSO) is prone to falling into local optimum and premature convergence in later operations, this paper reconstructs the concept of weighted aggregation based on a redefined similarity to describe the degree of diversity of the population, and adjusts the particle searching space with an adaptive decision to improve the global searching ability of PSO. Optimization ability, convergence speed and stability of the particle swarm algorithm are finally effectively improved. The experimental analysis further shows the effectiveness of the algorithm. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Agglomeration

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Deep learning-based transcription factor activity for stratification of breast cancer patients

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Biochimica et Biophysica Acta - Gene Regulatory Mechanisms 2022年第6期1865卷 194838页

作者： Xiong, Yuqiang Wang, Shiyuan Wei, Haodong Li, Hanshuang Lv, Yingli Chi, Meng Su, Dongqing Lu, Qianzi Yu, Yao Zuo, Yongchun Yang, Lei College of Bioinformatics Science and Technology Harbin Medical University Harbin 150081 China The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock College of Life Sciences Inner Mongolia University Hohhot 010070 China Department of Anesthesiology Harbin Medical University Cancer Hospital Harbin 150081 China Digital College Inner Mongolia Intelligent Union Big Data Academy Inner Mingolia Wesure Date Technology Co. Ltd. Hohhot 010010 China

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients. © 2022 Elsevier B.V.

关键词： Breast cancer Deep learning Prognosis Subtype Transcription factor

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An overview of image-based person re-identification

An overview of image-based person re-identification

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Luo, Peng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. In this paper, the image based traditional person re-ID is introduced from feature description and metric learning. In addition, with the extensive application of the deep learning algorithm in recent years, it also brings about the change of person re-ID algorithm. From the three aspects of loss function design, local feature and data augmentation, this paper introduces some work of person re-ID algorithm based on deep learning. Finally, it summarizes the development of pedestrian datasets in recent years, and looks forward to the future development trend of person re-ID. © Springer Nature Switzerland AG 2019.

关键词： Learning algorithms

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An improved person reidentification method by selective convolutional descriptor aggregation

An improved person reidentification method by selective conv...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

ISBN: (纸本)9783319987750

Person re-identification (person re-ID) is refers to pedestrians matching under a multi camera network in a non-overlapping field of view, that is, whether the pedestrian targets taken by different cameras are the same. Therefore, person re-ID is also a sub problem of image retrieval. In this paper, fine grained image retrieval method SCDA (Selective Convolutional Descriptor Aggregation) is used in person re-ID, and the framework of identification-verification is improved. The method can select useful deep descriptors, at the same time, remove background by localizing the main object. © Springer Nature Switzerland AG 2019.

关键词： Computer vision

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Comparison Between Back Projection Algorithm and Range Migration Algorithm in Terahertz Imaging

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IEEE ACCESS 2020年 8卷 18772-18777页

作者： Wang, Guanwen Qi, Feng Liu, Zhi Liu, Cuiling Xing, Chungui Ning, Wei Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110000 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Optoelect Informat Proc Shenyang 110016 Peoples R China Key Lab Image Understanding & Comp Vis Shenyang 110016 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110169 Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China Jilin Univ Coll Elect Sci & Engn Changchun 130012 Peoples R China

Back projection algorithm (BPA) and range migration algorithm (RMA) are two common approaches for image reconstruction in synthetic aperture radar (SAR) imaging. Imaging is implemented in spatial domain and spatial frequency domain, respectively and these two methods are often considered to generate the same results. This is true in normal microwave/millimeter wave SAR imaging systems, since the object of interests is located far away and the applied wavelength is long. In this case, the location of the object is the most important issue and there is no impetus to image the object with very fine details. In case of terahertz (THz) imaging, we have found some differences by using the two algorithms above, including the position of the object and resolution in 2D and 3D imaging. Some insights are given and we believe there should be some guidelines regarding the pros and cons before choosing the exact algorithm, especially when high-performance imaging is pursued.

关键词： Ambient environment sensing THz imaging image reconstruction

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A survey of multi-object video tracking algorithms

A survey of multi-object video tracking algorithms

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Zhou, Shuren Ke, Maolin Qiu, Jia Wang, Jin Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9783319987750

Video multi-object tracking is one of the important research topics in the field of computer vision, which is widely used in military and civil areas. At present, the research of single object tracking algorithm is quite mature, however the research of multi-object tracking is still ongoing. This paper focuses on four important stages in the multi-object tracking process: feature extraction, detector, data association and the tracker. The feature extraction part introduces the current methods of feature extraction, as well as the merits and demerits of each method;In the stage of detection, the tracking effect of the object appearance model in specific applications is described, and then the paper analyze the multi-object tracking algorithm based on detection and tracking as well as the multi-object tracking algorithm based on deep learning;In the tracking stage, the establishment of object motion model and multi-object tracking with different tracker hybrid algorithm are introduced;During the stage of data association, the paper introduce the multi-object tracking based on energy minimization and commonly used data association algorithm, respectively. Then the current mainstream datasets and evaluation methods are introduced. Finally, the future development of the multi-object tracking is discussed and forecasted. © Springer Nature Switzerland AG 2019.

关键词： Feature extraction

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Identifying eclipsing binary stars with TESS data based on a new hybrid deep learning model

arXiv

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arXiv 2025年

作者： Shan, Ying Chen, Jing Zhang, Zichong Wang, Liang Zou, Zhiqiang Li, Min College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China Nanjing Institute of Astronomical and Optics & Technology Chinese Academy of Sciences Nanjing210042 China CAS Key Laboratory of Astronomical Optics & Technology Nanjing Institute of Astronomical Optics & Technology Nanjing210042 China University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Jiangsu Nanjing211135 China

Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer’s perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs. © 2025, CC BY-SA.

关键词： Convolutional neural networks

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