检索结果-内蒙古大学图书馆

IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

来源：评论

学校读者我要写书评

暂无评论

An Effective Machine Learning Approach with Hyperparameter Tuning for Sentiment Analysis

引用

Data Intelligence 2025年第1期7卷 70-94页

作者： Saima Kanwal Ali Raza Chunyan Bai Dawei Zhang Jing Wen Dileep Kumar Engineering Research Centre of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and TechnologyNo.516 Jun Gong RoadShanghai 200093China Department of Mathematics School of SciencesHebei University of TechnologyBeichen CampusTianjin 300401China Shanghai Publishing and Printing College Shanghai 200093China Zhangjiang Laboratory Shanghai 200120China Department of Electronic Engineering Faculty of EngineeringThe Islamia University of Bahawalpur63100PunjabPakistan

Sentiment analysis depends on individuals’comments and opinions on *** from social media platforms like Twitter,Quora,or Facebook poses challenges due to informal language,including acronyms,misspellings,and ambiguous ***,hyperparameters in machine learning models significantly impact *** address these issues,we propose advanced feature engineering techniques in Natural Language Processing(NLP)and hyperparameter optimization to enhance prediction accuracy and generalization *** study employs Naïve Bayes,Logistic Regression(LR),Multi-layer Perceptron(MLP),and Support Vector Machine(SVM)to classify sentiments in tweets about Elon Musk’s potential acquisition of *** dataset,consisting of 100,000 tweets,is fetched using the Twitter representational state transfer application programming interface(REST API).We outline a sentiment analysis procedure to classify unstructured Twitter data,identify influential keywords,and categorize sentiments as Positive,Negative,or *** a hybrid Lexicon NLP approach,we extract contextually significant emotionally charged words and assign sentiment *** optimization via automated search methods ensures alignment with classifier performance *** achieved an impressive accuracy rate of 97%.Cross-validation minimizes random variations,providing a reliable assessment of the model’s generalization capabilities,and demonstrating the method’s accuracy in predicting sentiments with larger new unseen standard datasets,and varying sentiment.

关键词： hybrid lexicon Logistic regression(LR) Machine learning Natural language processing(NLP)

来源：评论

学校读者我要写书评

暂无评论

Arbitrarily High-Order Energy-Preserving Schemes for the Camassa-Holm Equation Based on the Quadratic Auxiliary Variable Approach

引用

Advances in Applied Mathematics and Mechanics 2023年第5期15卷 1233-1255页

作者： Yuezheng Gong Qi Hong Chunwu Wang Yushun Wang School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjingJiangsu 211106China Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjingJiangsu 211106China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical SciencesNanjing Normal UniversityNanjingJiangsu 210023China

In this paper,we present a quadratic auxiliary variable(QAV)technique to develop a novel class of arbitrarily high-order energy-preserving algorithms for the Camassa-Holm *** QAV approach is first utilized to transform the original equation into a reformulated QAV system with a consistent initial *** the reformulated QAV system is discretized by applying the Fourier pseudo-spectral method in space and the symplectic Runge-Kutta methods in time,which arrives at a class of fully discrete *** the consistent initial condition,they can be rewritten as a new fully discrete system by eliminating the introduced auxiliary variable,which is rigorously proved to be energy-preserving and *** numerical experiments are conducted to confirm the expected order of accuracy,conservative property and efficiency of the proposed *** presented numerical strategy makes it possible to directly apply a special class of Runge-Kutta methods to develop energy-preserving algorithms for a general conservative system with any polynomial energy.

关键词： Camassa-Holm equation quadratic auxiliary variable high-order energy-preserving schemes symplectic Runge-Kutta methods

来源：评论

学校读者我要写书评

暂无评论

Constrained temperature and relative humidity predictive control:Agricultural greenhouse case of study

引用

Information Processing in Agriculture 2024年第3期11卷 409-420页

作者： Hafsa Hamidane Samira EL Faiz Iliass Rkik Mohamed El Khayat Mohammed Guerbaoui Abdelali Ed-Dahhak Abdeslam Lachhab Computer Engineering and Intelligent Electrical Systems Laboratory ModelingMaterials and Control of Systems TeamHigher School of TechnologyMoulay Ismail University of MeknesMorocco

The importance of Model Predictive Control(MPC)has significant applications in the agricultural industry,more specifically for greenhouse’s control ***,the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the *** methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed *** this paper,we introduce an application of Constrained Model Predictive Control(CMPC)for a greenhouse temperature and relative *** this purpose,two Multi Input Single Output(MISO)systems,using Numerical Subspace State Space System Identification(N4SID)algorithm,are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation *** this sense,linear state space models were adopted in order to evaluate the robustness of the control *** the system is identified,the MPC technique is applied for the temperature and the humidity *** results show that the regulation of the temperature and the relative humidity under constraints was guaranteed,both parameters respect the ranges 15℃≤T_(int)≤30℃and 50%≤H_(int)≤70%*** the other hand,the control signals uf and uh applied to the fan and the heater,respect the hard constraints notion,the control signals for the fan and the heater did not exceed 0≤uf≤4.3 Volts and 0≤uh≤5 Volts,respectively,which proves the effectiveness of the MPC and the tracking ***,we show that with the proposed technique,using a new optimization toolbox,the computational complexity has been significantly *** greenhouse in question is devoted to Schefflera Arboricola cultivation.

关键词： Constrained model predictive control Greenhouse MISO systems optimization Relative humidity Temperature

来源：评论

学校读者我要写书评

暂无评论

A Unified Framework for Analysis and optimization of RIS-Assisted MIMO Multiple Access Networks

引用

IEEE Internet of Things Journal 2025年第13期12卷 25224-25239页

作者： Wang, Siqiang Zheng, Zhong Guo, Jing Fei, Zesong Sun, Zhi Zhang, Qin Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Institute of Technology School of Information and Electronics National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Tsinghua University Department of Electronic Engineering Beijing China

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated. © 2014 IEEE.

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

Research on multi-task assignment method of multi-configuration UAV based on improved hybrid algorithm

Research on multi-task assignment method of multi-configurat...

引用

2024 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Zheng, Zitong Xia, Qingyuan Li, Shengwei Deng, Bohai Nanjing University of Science and Technology Nanjing China National Key Laboratory of Rotorcraft Aeromechanics Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510685840

In this paper, an improved hybrid algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is formed to solve the problem of "premature" and slow convergence. First of all, this paper improves the multi-task Collaborative assignment model (CMTAP), comprehensively considers the task completion, sequence, time, UAV type, quantity, energy consumption and other factors, and establishes a scientific and standardized assignment model that meets the task requirements. Secondly, for the problem of target allocation, this paper proposes an improved hybrid particle swarm optimization algorithm (PSO), which is based on particle swarm optimization (PSO), and uses genetic algorithm (GA) to increase the global search ability of the algorithm for the problem that the optimization of discrete nonlinear functions is very easy to fall into the local optimal solution. The mixed coding method of traditional coding and binary coding is used to solve the problem of uncertain number of unmanned aerial vehicles (UAVs). At the same time, the PSO algorithm is reduced by increasing the inertia weight, increasing the time-varying factor, and adopting the adaptive mutation probability, bit mutation and cross mutation methods for GA to increase the convergence speed and global search ability of the hybrid algorithm. Finally, the overall cost of the improved hybrid algorithm, hybrid algorithm and traditional algorithm is compared by simulation, and the effectiveness and accuracy of the proposed algorithm are verified. © 2024 SPIE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

引用

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

来源：评论

学校读者我要写书评

暂无评论

Performance-Optimize Adaptive Robust Tracking Control for USV-UAV Heterogeneous systems With Uncertainty

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 7251-7262页

作者： Chen, Guangjun Wang, Wencheng Dong, Jiuxiang Northeastern University College of Information Science and Engineering Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Weifang University College of Machinery and Automation Weifang261061 China

This paper investigates the performance-optimized tracking control problem of the heterogeneous system consisting of the unmanned surface vehicle (USV) and the unmanned aerial vehicle (UAV) under uncertain dynamics. Based on the target tracking strategy, a performance optimization control framework for cooperative tracking in sea-air 3D space is established. To avoid the singularity of the UAV in the position loop inverse solution of the desired attitude, the saturation control strategy is designed to solve the UAV guidance problem, and the UAV position loop obtains bounded thrust. For the kinematic layer of the USV-UAV system, based on the prescribed performance control and optimal control methods, a prescribed performance-optimized control strategy is designed to optimize the virtual control input while ensuring the prescribed control performance. In addition, to reduce the impact of uncertainty on the USV-UAV control system, a prescribed performance compensation control scheme is proposed in the kinetic loop, which improves the USV-UAV control system robustness. The established sea-air 3D cooperative target tracking control framework can also be extended to complex formation control. Finally, experimental simulations verify the effectiveness of the designed control scheme. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Learning Neural Network Classifiers by Distributing Nearest Neighbors on Adaptive Hypersphere

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第1期6卷 234-249页

作者： Zhang, Xiaojing Liu, Shuangrong Wang, Lin Yang, Bo Fan, Jiawei University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing Jinan250022 China DataCanvas Laboratory Beijing100080 China Quan Cheng Laboratory Jinan250100 China The 15th Research Institute of China Electronics Technology Group Corporation Beijing100080 China

In this study, the adaptive hypersphere nearest neighbors (ASNN) method is proposed as an optimization framework to enhance the generalization performance of neural network classifiers. In terms of the classification task, the neural network draws decision boundaries by constructing the discriminative features of samples. To learn those features, attributed to the flexibility and separability, the pair-wise constraint-based methods that consist of the pair-wise loss and an embedding space (e.g., hypersphere space) have gained considerable attention over the last decade. Despite their success, pair-wise constraint-based methods still suffer from premature convergence or divergence problems, driven by two main challenges. 1) The poor scalability of the embedding space constrains the variety of the distribution of embedded samples, thereby increasing the optimization difficulty. 2) It is hard to select suitable positive/negative pairs during the training. In order to address the aforementioned problems, we propose an adaptive hypersphere nearest neighbors method. On the one hand, we improve the scalability of features via a scale-adaptive hypersphere embedding space. On the other hand, we introduce a neighborhood-based probability loss, which magnifies the difference between pairs and enhances the discriminative power of features generated by the neural networks based on the nearest neighbor-based pairing strategy. Experiments on UCI datasets and image recognition tasks demonstrate that the proposed ASNN not only achieves improved intraclass consistency and interclass separability of samples, but also outperforms its competitive counterparts. © 2024 IEEE.

关键词： Training optimization Artificial Neural Networks Artificial Intelligence Scalability Adaptive systems Vectors Nearest Neighbor methods Fans Encoding Classification Neighborhood Based Probability Loss Neural Network Pairing Strategy Scale Adaptive Hypersphere

来源：评论

学校读者我要写书评

暂无评论

RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks

引用

IEEE Transactions on Smart Grid 2025年第4期16卷 3419-3431页

作者： Yang, Xu Lin, Chenhui Liu, Haotian Wu, Wenchuan Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China

As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared to traditional distribution networks. Although advanced reinforcement learning (RL) methods, which alleviate the burden of complicated modelling and optimization, have greatly improved the efficiency of energy management in ADNs, safety becomes a critical concern for RL applications in real-world problems. Since the design and adjustment of penalty functions, which correspond to operational safety constraints, require extensive domain knowledge in RL and power system operation, the emerging ADN operators call for a more flexible and customized approach to address the penalty functions so that the operational safety and efficiency can be further enhanced. Empowered with strong comprehension, reasoning, and in-context learning capabilities, large language models (LLMs) provide a promising way to assist safe RL for energy management in ADNs. In this paper, we introduce the LLM to comprehend operational safety requirements in ADNs and generate corresponding penalty functions. In addition, we propose an RL2 mechanism to refine the generated functions iteratively and adaptively through multi-round dialogues, in which the LLM agent adjusts the functions pattern and parameters based on training and test performance of the downstream RL agent. The proposed method significantly reduces the intervention of the ADN operators. Comprehensive test results demonstrate the effectiveness of the proposed method. © 2010-2012 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：