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2023 IEEE International Conference on Big Data, BigData 2023

作者： De Santis, Francesco Giordano, Danilo Mellia, Marco Damilano, Alessia Politenico di Torino Department of Control and Computer Engineering Torino Italy Bitron Electronic Division Quality and Process Digitalization Deparment Grugliasco Italy

ISBN: (纸本)9798350324457

In the era of Big Data, effective data reduction through feature selection is of paramount importance for machine learning. This paper presents GLEm-Net (Grouped Lasso with Embeddings Network), a novel neural framework that seamlessly processes both categorical and numerical features to reduce the dimensionality of data while retaining as much information as possible. By integrating embedding layers, GLEm-Net effectively manages categorical features with high cardinality and compresses their information in a less dimensional space. By using a grouped Lasso penalty function in its architecture, GLEm-Net simultaneously processes categorical and numerical data, efficiently reducing high-dimensional data while preserving the essential information. We test GLEm-Net with a real-world application in an industrial environment where 6 million records exist and each is described by a mixture of 19 numerical and 7 categorical features with a strong class imbalance. A comparative analysis using state-of-the-art methods shows that despite the difficulty of building a high-performance model, GLEm-Net outperforms the other methods in both feature selection and classification, with a better balance in the selection of both numerical and categorical features. © 2023 IEEE.

关键词： categorical features data reduction embeddings feature selection neural network

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Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

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computers, Materials & Continua 2023年第12期77卷 3095-3117页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of ***,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social *** bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation ***,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes ***,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their *** the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability *** process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based ***,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node ***,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关键词： Temporal social networks influence maximization voting strategy interactive properties self-similarity

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Predicting Age from Brain Connectivity Matrix: A Stacked Regression Approach

Predicting Age from Brain Connectivity Matrix: A Stacked Reg...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Gabriel Pojoga Victor Teslaru Otilia ZvoriȘteanu Ștefan-Daniel Achirei Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

Accurately predicting age from functional connectome data can provide valuable insights into brain development and neurodegenerative processes. In this study, we apply various machine learning algorithms to age prediction using functional connectivity features. The preprocessing pipeline included missing value imputation and categorical variable encoding. We experimented with traditional regression models and neural networks, ultimately achieving the best performance with a stacking ensemble. The ensemble leveraged a Multi-Layer Perceptron meta-learner to reduce base model biases, improving predictive accuracy. To enhance diversity, base models were selected based on their individual performance and prediction correlation. Our results demonstrate that ensemble learning can effectively handle the complexity of neuroimaging data, offering a robust approach for age prediction and potential applications in neurological research.

关键词： Neuroimaging Computational modeling Precision medicine Stacking Diversity reception Proteomics Predictive models Brain modeling Ensemble learning Biological neural networks

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Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming 24

Enhanced cyber-physical system with semantic technologies an...

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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Ciolofan, Sorin N. Dragoicea, Monica Popeanga, Daniel-Catalin Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical Systems (CPS) to support smart farming and sustainable agriculture. © 2023 IEEE.

关键词： Semantics

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Multi-task Collaborative Network for Image-Text Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qin, Xueyang Li, Lishuang Hao, Jing Ge, Meiling Huang, Jiayi Pang, Guangyao School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer Engineering Weifang University Weifang261061 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image-text retrieval aims to capture semantic relevance between images and texts. Most existing approaches rely solely on the image-text pairs to learn visual-semantic representation through fine-grained alignments while neglecting the potential beneficial impact of unimodal tasks on cross-modal retrieval. To this end, we present a Multi-Task Collaborative Network (MTCN) that leverages the synergy between multiple tasks to enhance the performance of image-text retrieval. Specifically, we introduce three unimodal tasks, including text-text matching, image multi-label classification, and text multi-label classification, and train together with target task image-text retrieval from the perspective of semantic constraints. Additionally, we employ a modality interaction module for image-text retrieval to discover interrelationships between these two modalities. Subsequently, a cascaded graph convolutional network combined with a multi-layer perceptron is used to infer the correlation scores between images and texts. We conduct comprehensive experiments on two benchmark datasets, Flickr30K and MSCOCO, and the quantitative and qualitative experimental results demonstrate the effectiveness of the proposed method. The source code is available at https://***/FlyCuteBird/MTCN. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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A Mean Pyramid Strategy for Binary Pattern Family 11

A Mean Pyramid Strategy for Binary Pattern Family

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Xu, Xiaochun Shen, Yifan Lei, Jiaxing Wang, Xin Guo, Ente Minjiang University College of Computer and Control Engineering Fuzhou350108 China Minjiang University International Digital Economy College Fuzhou350108 China

ISBN: (纸本)9798350326277

This paper proposes a novel mean pyramid strategy for binary pattern family. The mean pyramid strategy can help the binary pattern family to capture robust multilayer local texture structure instead of the traditional single pixel value. To validate the effectiveness of the proposed strategy, we apply the mean pyramid strategy to the classical completed local binary pattern and present the completed mean pyramid local binary pattern for texture classification tasks. The experimental results on Outex database show that the proposed mean pyramid strategy can significantly enhances the classification performance of binary pattern family. © 2023 IEEE.

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State-of-Health Estimation of Lithium-Ion Battery Based on Linguistic Description Considering Capacity Regeneration

State-of-Health Estimation of Lithium-Ion Battery Based on L...

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2023 China Automation Congress, CAC 2023

作者： Cui, Fangshu Li, Hui Wu, Kangjia Fan, Sitong Shi, Yuanhao School of Computer Science and Technology North University of China Taiyuan China School of Electrical and Control Engineering North University of China Taiyuan China

ISBN: (纸本)9798350303759

Owing to the wide application of lithium-ion batteries in industry, it is of great significance for accurate prediction of battery state of health (SOH) to ensure the safety and stability of equipment. The battery capacity regeneration (CR) during capacity degradation affects the accuracy of capacity prediction. This study proposes a hybrid estimation method for state of health of lithium-ion batteries based on linguistic description (LD) considering capacity regeneration. First, the capacity degradation data is decoupled into the trend component and the capacity regeneration component by the variational mode decomposition (VMD). Second, in order to improve the accuracy and reliability of battery capacity prediction, the capacity regeneration part is described by linguistic description. Finally, the trend part and the linguistic description sequence of capacity regeneration are predicted by support vector regression (SVR) model and long short term memory network with attention mechanism (Attn-LSTM) respectively, and their forecast results are superposed as the battery capacity prediction. The experimental results in NASA battery data demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Lithium-ion batteries

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Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation

Behavioural Swarm Optimisation for Stable Slung-Load Aerial ...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Chen, Jingyu Oyekan, John The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of York Department of Computer Science York United Kingdom

ISBN: (纸本)9798350314588

Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation. © 2023 IEEE.

关键词： Motion planning

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LCD Graphic Element Location Method Combining NL-means and Local Threshold Segmentation 3

LCD Graphic Element Location Method Combining NL-means and L...

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3rd International Symposium on computer Technology and Information Science, ISCTIS 2023

作者： Wang, Xiaohui Tan, Jianxiong Shenke Technology Group Co. Shijiazhuang China School of Control and Computer Engineering North China Electric Power University Baoding China

ISBN: (纸本)9798350325386

Accurate positioning of screen primitives is crucial in the machine vision-based automatic detection of intelligent water meter LCD screens. Detecting edge details of the LCD screen using the A component of the LAB color space improves the accuracy of LCD screen area positioning. This approach reduces background interference from Gaussian noise, non-uniform lighting, specular reflection, and local highlights. This study proposes a technique that combines NL-means and Sauvola local threshold segmentation methods to locate LCD screen areas and graphics elements. The experimental results indicate that this technique satisfies the defect detection criteria for smart water meter LCD screens set by the enterprise. The LCD screen detection tool extracted a smart water meter LCD screen image with 98.4% accuracy in LCD screen element positioning. Compared to the median filter method, this represents a significant improvement, and the combination with the maximum between-class variance method further increases accuracy by 2.7%. © 2023 IEEE.

关键词： Liquid crystal displays

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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