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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Kou, Xuye Chen, Yitong Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Single-cell RNA sequencing allows to discovery of new cell subtypes based on transcriptomic information. Clustering analysis is an effective approach for exploring single-cell heterogeneity. Nevertheless, current single-cell clustering methodologies are challenged by the high-dimensional, sparse, and dropouts on the one hand, and on the other hand, they neglect the thorough exploration of potential relationships between cells and noise removal. To tackle these challenges, this study introduces a novel self-optimized single-cell clustering algorithm named scZVEA, which combines Zero- Inflated Negative Binomial (ZINB) model and variational graph attention autoencoder. The scZVEA framework comprises three key modules. Firstly, a Deep Count Autoencoder (DCA) is designed to model data distribution and eliminate noise. Then, a variational graph attention autoencoder to extract latent features from the data. Lastly, the self-optimized clustering module enables the two previously independent clustering and feature modules to mutually benefit each other by iteratively updating cluster centers to further enhance clustering performance. Experimental findings based on six authentic scRNAseq datasets illustrate that the proposed clustering algorithm significantly enhances clustering accuracy when compared to alternative methods. © 2024 SPIE.

关键词： Clustering algorithms

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FF-LSTM: Phenotype prediction based on feature fusion 3

FF-LSTM: Phenotype prediction based on feature fusion

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3rd International Conference on Electronic Information Engineering and data processing, EIEDP 2024

作者： Zhang, Ge Zhang, Zhou Kou, Xuye Chen, Yitong School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510680531

Phenotypic prediction before crop planting and harvest facilitates plant phenotyping analysis and the implementation of precision agriculture, which is crucial for food security policy formulation, crop management, and food security. Environmental data collected in the field, as important factors affecting phenotypes, can be used together with genotypes to train phenotypic prediction models to improve their prediction capabilities. Although the rise of deep learning has provided a powerful tool for predicting phenotypes using genotype and environmental data, current research only performs simple splicing operations when processing genotype and environmental data and fails to effectively model the impact of genotype and environment on phenotype. Therefore, it is still challenging to accurately predict phenotype using genotype and environment. To solve the problems mentioned above, we design a phenotype prediction model named FF-LSTM. In this method, LSTM is used to extract features from SNP and environmental, and the feature fusion method weights the extracted features to simulate the influence of genotype and environment on phenotype. We conducted experiments on the dataset and verified the effectiveness of this method by analyzing and evaluating the experimental results. © 2024 SPIE.

关键词： Forecasting

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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Sparse Deep Neural Network for Nonlinear Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第1期16卷 58-78页

作者： Yuesheng Xu Taishan Zeng Department of Mathematics and Statistics Old Dominion UniversityNorfolkVirginia 23529USA School of Mathematical Science South China Normal UniversityGuangzhou 510631China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

More competent learning models are demanded for data processing due to increasingly greater amounts of data available in *** that we encounter often have certain embedded sparsity *** is,if they are represented in an appropriate basis,their energies can concentrate on a small number of basis *** paper is devoted to a numerical study of adaptive approximation of solutions of nonlinear partial differential equations whose solutions may have singularities,by deep neural networks(DNNs)with a sparse regularization with multiple *** that DNNs have an intrinsic multi-scale structure which is favorable for adaptive representation of functions,by employing a penalty with multiple parameters,we develop DNNs with a multi-scale sparse regularization(SDNN)for effectively representing functions having certain *** then apply the proposed SDNN to numerical solutions of the Burgers equation and the Schrödinger *** examples confirm that solutions generated by the proposed SDNN are sparse and accurate.

关键词： Sparse approximation deep learning nonlinear partial differential equations sparse regularization adaptive approximation

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Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation

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Plant Phenomics 2023年第4期5卷 767-778页

作者： Fan Zhang Bo Wang Fuhao Lu Xinhong Zhang Huaihe Hospital of Henan University Kaifeng 475004China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004China State Key Laboratory of Crop Stress Adaptation and Improvement Henan UniversityKaifeng 475004China School of Software Henan UniversityKaifeng 475004China

Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves,which is important for *** deep learning-based plant stomata detection methods are based on horizontal *** detection anchor boxes of deep learning model are horizontal,while the angle of stomata is randomized,so it is not possible to calculate stomata traits directly from the detection anchor *** processing of image(e.g.,rotating image)is required before detecting stomata and calculating stomata *** paper proposes a novel approach,named DeepRSD(deep learning-based rotating stomata detection),for detecting rotating stomata and calculating stomata basic traits at the same ***,the stomata conductance loss function is introduced in the DeepRSD model training,which improves the efficiency of stomata detection and conductance *** experimental results demonstrate that the DeepRSD model reaches 94.3%recognition accuracy for stomata of maize *** proposed method can help researchers conduct large-scale studies on stomata morphology,structure,and stomata conductance models.

关键词： calculation. Deep rotating

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Solving Multiobjective Combinatorial Optimization via Learning to Improve Method

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第3期9卷 2122-2136页

作者： Ye, Te Zhang, Zizhen Zhang, Qingfu Chen, Jinbiao Wang, Jiahai Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510275 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Recently, neural combinatorial optimization (NCO) methods have been prevailing for solving multiobjective combinatorial optimization problems (MOCOPs). Most NCO methods are based on the "Learning to Construct"(L2C) paradigm, where the trained model(s) can directly generate a set of approximate Pareto optimal solutions. However, these methods still suffer from insufficient proximity and poor diversity towards the true Pareto front. In this paper, following the "Learning to Improve"(L2I) paradigm, we propose weight-related policy network (WRPN), a learning-based improvement method for solving MOCOPs. WRPN is incorporated into multiobjective evolutionary algorithm (MOEA) frameworks to effectively guide the search direction. A shared baseline for proximal policy optimization is presented to reduce variance in model training. A quality enhancement mechanism is designed to further refine the Pareto set during model inference. Computational experiments conducted on two classic MOCOPs, i.e., multiobjective traveling salesman problem and multiobjective vehicle routing problem, indicate that our method achieves remarkable results. Notably, our WRPN module can be easily integrated into various MOEA frameworks such as NSGA-II, MOEA/D and MOGLS, providing versatility and applicability across different problem domains. © 2025 IEEE.

关键词： Deep reinforcement learning

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A new double scrambling color image encryption algorithm based on multi chaotic systems and image segmentation 3

A new double scrambling color image encryption algorithm bas...

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3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Tian, Junfeng Wang, Ke Liu, Cheng Henan University Key Laboratory of Big Data Analysis and Processing of Henan Province Kaifeng China

ISBN: (纸本)9798350320404

Aiming at low security of traditional encryption methods, this paper give a new encryption method by using multi chaotic systems and image segmentation. Firstly, use Arnold transform to perturb the RGB component matrices of the source image. Subsequently, the chaotic image chunking algorithm is applied to the mixed component matrix, and the chunked mixed matrix is perturbed by Hyperchaotic Chen system and Logistic mapping. Moreover, Diffusion of pixel transformations to the whole image by inverse transform operations. Finally, logistic system is applied to the secondary pixels scrambling, which achieves image encryption. The experimental data indicate that this encryption method encryption effect is good, and can effectively prevent ciphertext attack and noise attack. © 2022 IEEE.

关键词： Image segmentation

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Kubernetes-based Scripted Remote Sensing Process Service Platform 6

Kubernetes-based Scripted Remote Sensing Process Service Pla...

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Zhou, Liming Liu, Mengyu Yang, Wencheng Liu, Zhehao Zhao, Hang Han, Hongyu Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781665460033

With the formation of the high-resolution satellite system, the information carried in remote sensing data is becoming more and more abundant, and the types of data are also increasing. In order to realize the efficient processing of remote sensing data, the construction of remote sensing process is very important. At the same time, higher requirements are put forward for the remote sensing system in terms of dynamic resource scheduling, easy maintenance, elastic expansion, controllable complexity, and flexible deployment. In response to the above problems, this paper first designs a scripted process construction technology, which realizes the efficient construction of various complex processes. And introduce Docker container technology and Kubernetes container cluster management technology, split the remote sensing system into different micro-service modules, run independently in mutually isolated containers, so that the system has the characteristics of flexible load scheduling and elastic scaling. © 2023 IEEE.

关键词： Containers

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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Computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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A Parallel Beamforming Algorithm for Sonar Signal processing

A Parallel Beamforming Algorithm for Sonar Signal Processing

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Dang, Lanxue Liu, Chongyang Yang, Kang Hou, Yan-E Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510664616

Sonar is a general term for various underwater acoustic devices, which are used to perform underwater target detection, positioning, identification, tracking and underwater communication, navigation, measurement and other tasks. All sonar devices including active sonar and passive sonar both require a signal processor to execute underwater acoustic signal processing. Beamforming is the main and core component of the sonar signal processor, and its processing speed affects the real-time performance of sonar. Thus, this paper first implements a parallel sonar beamforming algorithm to improve the processing speed of the beamforming algorithm. The proposed algorithm was carried out several experiments, and the experimental results demonstrate that the parallelism of multi-core CPU can bring better speedup ratio and efficiency than the traditional serial CPU algorithm. © 2023 SPIE.

关键词： Beamforming

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