检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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Fast Learning-Based Split Type Prediction Algorithm for VVC

Fast Learning-Based Split Type Prediction Algorithm for VVC

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IEEE International Conference on Image Processing

作者： Dayong Wang Liulin Chen Xin Lu Frederic Dufaux Weisheng Li Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications University of Electronic Science and Technology of China

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural networks for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average.

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Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

arXiv

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arXiv 2022年

作者： Lou, Wei Li, Haofeng Li, Guanbin Han, Xiaoguang Wan, Xiang Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Pazhou Lab Guangzhou510330 China

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

arXiv

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

arXiv

引用

arXiv 2023年

作者： Chen, Jiasheng Tang, Juan Yan, Ming Lai, Shuai Liang, Kun Lu, Jianguang Yang, Wenqiang Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Singapore Singapore State Key Laboratory of Public Big Data Guizhou University Guiyang China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as 10−6, and the absolute errors for algebraic variables is maintained at 10−5, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

arXiv

引用

arXiv 2023年

作者： Min, Wenwen Xu, Taosheng Ding, Chris The Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University Yunnan Kunming650091 China The Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230031 China The School of Data Science The Chinese University of Hong Kong Shenzhen China

Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an ∞/0-norm constrained weighted sparse PLS (∞/0-wsPLS) method for joint sample and feature selection, where the ∞/0-norm constrains are used to select a subset of samples. We prove that the ∞/0-norm constrains have the Kurdyka-Lojasiewicz property so that a globally convergent algorithm is developed to solve it. Moreover, multi-view data with a same set of samples can be available in various real problems. To this end, we extend the ∞/0-wsPLS model and propose two multi-view wsPLS models for multi-view data fusion. We develop an efficient iterative algorithm for each multi-view wsPLS model and show its convergence property. As well as numerical and biomedical data experiments demonstrate the efficiency of the proposed methods. © 2023, CC BY.

关键词： data fusion

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Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives

arXiv

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arXiv 2024年

作者： Wu, Xuan Wang, Di Wen, Lijie Xiao, Yubin Wu, Chunguo Wu, Yuesong Yu, Chaoyu Maskell, Douglas L. Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin university Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore School of Software Tsinghua University Beijing100084 China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted, they did not cover the state-of-the-art (SOTA) NCO solvers emerged recently. More importantly, to establish a comprehensive and up-to-date taxonomy of NCO solvers, we systematically review relevant publications and preprints, categorizing them into four distinct types, namely Learning to Construct, Learning to Improve, Learning to Predict-Once, and Learning to Predict-Multiplicity solvers. Subsequently, we present the inadequacies of the SOTA solvers, including poor generalization, incapability to solve large-scale VRPs, inability to address most types of VRP variants simultaneously, and difficulty in comparing these NCO solvers with the conventional Operations Research algorithms. Simultaneously, we discuss on-going efforts, identify open inadequacies, as well as propose promising and viable directions to overcome these inadequacies. Notably, existing efforts focus on only one or two of these inadequacies, with none attempting to address all of them concurrently. In addition, we compare the performance of representative NCO solvers from the Reinforcement, Supervised, and Unsupervised Learning paradigms across VRPs of varying scales. Finally, following the proposed taxonomy, we provide an accompanying web page as a live repository for NCO solvers. Through this survey and the live repository, we aim to foster further advancements in the NCO community. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Joint Design for Cramér-Rao Bound and Secure Transmission in Semi-IRS Aided ISAC Systems

Joint Design for Cramér-Rao Bound and Secure Transmission i...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Wenjing Wei Xiaowei Pang Xiaoqi Qin Shiqi Gong Chengwen Xing Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China College of Computing & Data Science (CCDS) Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

We study a semi-passive intelligent reflecting surface (IRS) enabled ISAC, where IRS is employed to assist the secure communication and perform target sensing. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. We derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve the performance tradeoff, we design a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes.

关键词： Measurement Vehicular and wireless technologies Direction-of-arrival estimation Array signal processing Simulation Estimation Sensors

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