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An image segmentation fusion algorithm based on density peak clustering and Markov random field

Multimedia Tools and Applications 2024年第37期83卷 85331-85355页

作者： Feng, Yuncong Liu, Wanru Zhang, Xiaoli Zhu, Xiaoyan College of Computer Science and Engineering Changchun University of Technology Jilin Changchun130012 China Artificial Intelligence Research Institute Changchun University of Technology Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively capture intricate relationships among pixels. However, MRF typically requires an initial labeling field, and the number of classifications needs to be manually selected. To tackle these issues, we propose a novel medical image segmentation algorithm based on density peak clustering (DPC) and Markov random fields. Firstly, we improve DPC to make it applicable to grayscale images, named GIDPC. In the GIDPC method, local gray density and gray bias are defined to enable the automatic determination of the number of classifications. Then, GIDPC and MRF are combined to achieve image segmentation. Furthermore, a segmentation fusion method is employed to enhance the accuracy of image segmentation. We conduct comparison experiments on the whole brain atlas image library. Our proposed algorithm achieves high average values in uniformity measure, accuracy, precision and sensitivity, respectively. Experimental results demonstrate that the proposed algorithm outperforms other image segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Optimal Design and Mechanical Simulation of Rubber bushing with Convex Hull Structure Based on Bionics

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Journal of Bionic engineering 2023年第5期20卷 2018-2029页

作者： Ce Liang Min Li Jicai Liang Shaoqiang Wang Qigang Han Yi Li Key Laboratory of Automobile Materials Ministry of Educationand College of Materials Science and EngineeringJilin UniversityChangchun130025China Roll Forging Research Institute Jilin UniversityChangchun130025China School of Computer Science and Technology Changchun UniversityChangchun130022China

Inspired by the safe landing of cats falling from high altitudes,a bionic flexible rubber bushing structure is proposed and its motion characteristics are systematically studied to explore its potential application in the suppression of *** convex hull structure on the bushing surface is abstracted from the cat’s claw pad,and the hyper-viscoelastic model is selected as the constitutive model of the rubber *** addition,the design with the best vibration damping effect is finally obtained by reasonably adjusting the amount of radial compression and distribution of bionic ***,under the same conditions,the test results of the dynamic characteristics of the bushing verify the accuracy of the simulation *** results show that the convex hull bionic structure designed in this paper can effectively change the motion characteristics of the rubber bushing under various working conditions,which provides new inspiration or potential possibility for the design of rubber bushing in the future.

关键词： Rubber bushing Bionic design Finite element analysis Vibration isolator Radial compression

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Asynchronous distribution scheme of group quantum keys based on max heap in classical network

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Journal of King Saud University - computer and Information sciences 2025年第4期37卷 1-17页

作者： Zhu, Dexin Zhao, Zilong Zhang, Huanjie Zhou, Zhiqing Li, Yuanbo Zhao, Jian Song, Lijun Zheng, Jun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing 100081 China College of Computer Science and Technology Changchun University Jilin Changchun 130022 China Jilin Engineering Laboratory for Quantum Information Technology Changchun Institute Of Technology Jilin Changchun 130022 China

With the advancement of the information age, various information-based products have become increasingly prevalent, leading to a corresponding rise in the volume of data requiring encrypted transmission. Consequently, secure key distribution has become particularly critical, especially in large-scale communication scenarios. Considering practical limitations such as offline availability and low performance among communication participants, we propose an asynchronous group quantum key distribution scheme based on a max heap structure and blockchain technology within classical networks. In this scheme, quantum keys serve as group communication keys, and the encrypted transmission of quantum keys is facilitated through a max heap constructed according to user information. Specifically, during the initialization phase, users upload relevant information to the Key Distribution Center (KDC), while in the key distribution phase, the blockchain stores encrypted temporary group keys, enabling asynchronous access. The proposed scheme not only reduces performance consumption for users within the group but also leverages the intrinsic properties of the max heap to dynamically balance loads. Moreover, the scheme achieves both forward and backward secrecy and demonstrates favorable performance in terms of storage efficiency and computational cost. © The Author(s) 2025.

关键词： Blockchain Group key distribution Max heap Quantum key

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CT Image Segmentation Algorithm for Liver Tumors Based on 3D AGCH-Net and Multi-Scale Fusion 14

CT Image Segmentation Algorithm for Liver Tumors Based on 3D...

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14th International Conference on Electronics, Communications and Networks, CECNet 2024

作者： Zhang, Lihui Fu, Zihan Liu, Yingxue Wang, Xiaoyu Zhang, Lijuan School of Management Engineering Jilin University of Architecture and Technology Changchun130114 China College of Internet of Things Engineering Wuxi University Wuxi214105 China School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781643685632

Liver tumor segmentation remains a challenging task. Previous methods often relied on simple feature fusion (e.g., concatenation or summation), leading to suboptimal results. This paper proposes a segmentation method based on AGCHNet with multi-scale feature fusion. Built on the U-Net framework, we replace standard convolutions with Fused-MBConv, improving multi-scale feature extraction and representation. The SE_ASPP module at the bottleneck enhances salient feature learning, while the AGCH attention mechanism in skip connections mitigates semantic conflicts and suppresses irrelevant information. Our method, evaluated with five-fold cross-validation on the LiTs dataset, achieved 96.6% Dice, 7.3 mm HD, and 1.0 mm ASD for liver segmentation. For tumor segmentation, it reached 80.9% Dice, 14.3 mm HD, and 2.3 mm ASD, outperforming mainstream methods. © 2024 The Authors.

关键词： Convolution

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of computer science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： Information centric networking caching strategy two-layer caching model hierarchical division

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Optimizing YOLOv8-IC network for industrial scene recognition under camera surveillance

Optimizing YOLOv8-IC network for industrial scene recognitio...

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2024 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhang, Jiaming Chen, Jingtian Zhao, Peng Li, Yingxuan Yu, Biao Zhou, You JILIN Institute of Electronic Information Products Inspection Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

ISBN: (纸本)9781510689275

In the context of industrial automation and intelligent monitoring, accurate identification of the industrial environment is crucial to risk management. However, traditional scene classification methods struggle to capture the features of diverse structures and irregular shapes in complex industrial scenes. Additionally, they have shortcomings in extracting details and defining boundaries under dynamic lighting conditions. Deep learning-based methods, however, are better equipped to adapt to the complex backgrounds and dynamic detail variations in industrial environments through automatic feature learning. Although the YOLOv8 network benefits from the advantages of deep learning models, it still has limitations in meeting the recognition needs of complex industrial scenes. These limitations include difficulty in effectively capturing irregular structures and details, low positioning accuracy for overlapping boundaries, and susceptibility to interference from complex backgrounds. To address these issues, this paper introduces YOLOv8-IC (Industrial Classification), an improved version of YOLOv8, to meet the specific requirements of industrial scene classification. This enhancement improves feature extraction, target positioning, and key area focusing capabilities in complex environments. To further validate the model's effectiveness, a new dataset, Places24, was constructed by combining industrial scenes from the Places365 dataset with self-collected camera monitoring images. This approach enhances the model's applicability to real industrial environments. Experimental results show that the YOLOv8-IC network outperforms the original model in classification accuracy and real-time performance, significantly enhancing the stability and accuracy of industrial scene classification. The proposed YOLOv8-IC-based industrial scene classification scheme offers a more accurate solution for intelligent industrial monitoring systems. © 2025 SPIE.

关键词： Risk management

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Research on Global Illumination Exploration in Dynamic Scenes 3

Research on Global Illumination Exploration in Dynamic Scene...

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3rd International Conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Ji, Xiu Yang, Huamin Han, Cheng School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China School of Electrical and Information Engineering Changchun Institute of Technology Jilin Changchun130012 China

ISBN: (纸本)9781510667563

Neural rendering can achieve satisfactory illumination effects with unknown light source locations and brightness. When training on dynamic scenarios (where object positions, textures, lighting, and viewpoints can vary), variations in object position, material properties, light intensity, and angle make the training results unsatisfactory due to the influence of global light sources. This paper proposes a novel global illumination exploration method, which uses Markov Chain Monte Carlo (MCMC) to perform small sample sampling of light points, and incorporates a new Bayesian optimization (Randomized Search) strategy to optimize the sample data combined with real-time data to remove sampling redundant information and improve training efficiency. The comprehensive experimental results show that our proposed method provides a practical and effective solution for the study of global illumination in dynamic scenes. © 2023 SPIE.

关键词： Light sources

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Depth map cavity repair algorithm based on RGB image 8

Depth map cavity repair algorithm based on RGB image

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Sheng, Yiyang Fu, Haohai Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China School of Computer Science and Technology Changchun Institute of Technology Changchun China

ISBN: (纸本)9798350302455

The 3D reconstruction process often requires high-precision depth images. However, based on the limitations of the current depth sensor's own equipment and the influence of shootingobjects, environment and other factors, the obtained depth map often has cavities and noise. Aiming at the problem that the traditional depth map cavity repair algorithm can not take into account the cavity filling and object edge preservation, and can noteffectively use the RGB image information obtained synchronously, a cavity repair algorithm based on RGB image is proposed. The algorithm divides the cavities in the depth map into edge parts and non-edge parts by obtaining the edge information in the RGB image, and then uses different methods to repair the two parts of the cavities:For non-edge cavities, because the neighborhood depth value changes continuously, the neighborhood depth value is directly used for repair;for the cavity at the edge, due to the discontinuous change of the neighborhood depth value, the depthvalue corresponding to the pointwith the largest distance centerweight in the neighborhood is selected to repair the cavity by using the pixel points corresponding to the cavity in the RGB image and its neighborhood. The experimental results show that the algorithm makes full use of the RGB image information, which can accurately fill the depth map cavitys and effectively avoid the edge blur of the object. © 2023 IEEE.

关键词： Repair

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