检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Pinto, Diego Maria Russo, Davide Donato Sudoso, Antonio M. Institute for Systems Analysis and Computer Science "Antonio Ruberti" National Research Council Via dei Taurini 19 Rome00185 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), such as parks, green walls, roofs, and street trees, offer a promising means to regulate urban temperatures and enhance air quality. However, determining their optimal placement to maximize environmental benefits remains a pressing challenge. Leveraging Operational Research (OR) tools, we propose a Mixed-Integer Linear Programming (MILP) model that integrates multiple factors, including urban challenges, physical constraints, clustering techniques, convolution theory, and fairness considerations. This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces. Through several case study analyses, we demonstrate the effectiveness of our approach in improving environmental and social indicators. This research holds implications for policy and practice, empowering urban planners and policymakers to make informed decisions regarding NBS implementation. Such decisions ensure that investments in urban greening yield maximum environmental, social, and economic benefits. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Sentiment Classification of Chinese Railway Review Text Based on Multi-Feature Fusion Gated Recurrent Unit

Sentiment Classification of Chinese Railway Review Text Base...

引用

2021 International Conference on Information science, Parallel and Distributed Systems, ISPDS 2021

作者： Su, Chenglang Jiang, Lekang Pi, Shaorui Shenzhen University College of Mechatronics and Control Engineering Shenzhen China University of Nottingham Department of Computer Science Nottingham United Kingdom University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom

ISBN: (纸本)9781665417389

Posting comments through online platforms has become the main channel for many users to express their opinions. Many Chinese private companies have rectified their marketing strategies by collecting online user comments and using them as a reference. However, since many Chinese state-owned enterprises are within the system, they can only rectify and reform according to the plan provided by the government but with little effect. China's railway system is faced with such problems. Traditional sentiment classification methods may encounter some problems when processing short texts. Our research provides a Gated Recurrent Unit (GRU) sentiment classification method based on multi-feature fusion and sentiment classification of user reviews about China's railway system. And through classification, we get 1244 comments with negative emotions and 919 non-emotional comments © 2021 IEEE.

关键词： Railroads

来源：评论

学校读者我要写书评

暂无评论

Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems

arXiv

引用

arXiv 2023年

作者： Koelewijn, Patrick J.W. Singh, Rajiv Seiler, Peter Tóth, Roland Sioux Technologies B.V. Eindhoven Netherlands Control Systems Group Eindhoven University of Technology Eindhoven Netherlands The MathWorks Inc. Natick United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear dynamical systems

来源：评论

学校读者我要写书评

暂无评论

Interpretable deep temporal neural networks for in-situ monitoring under varying conditions in micro-electrical discharge machining

引用

Journal of Manufacturing Systems 2025年 81卷 222-237页

作者： Ye, Long Guo, Cheng Wu, Ming Qian, Jun Yu, Nan Reynaerts, Dominiek Institute of Materials and Processes School of Engineering University of Edinburgh EdinburghEH9 3FB United Kingdom Micro & Precision Engineering Group Department of Mechanical Engineering KU Leuven Leuven3001 Belgium Members Flanders Make Leuven Belgium Department of Computer Science KU Leuven Leuven3001 Belgium Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

Monitoring is critical enabler of digitalization in modern manufacturing, supporting enhanced process control, quality assurance, and real-time decision-making. By integrating data-driven techniques with the powerful capabilities of deep learning, monitoring systems can efficiently extract valuable insights from complex, high-dimensional time-series data. However, traditional data-driven approaches often lack interpretability, limiting their adoption in industrial applications that demand high reliability and accountability. To address this challenge, this paper proposes an interpretable monitoring framework based on a deep temporal neural network (DTNN). Designed with a modular architecture, the DTNN integrates key components for embedding, temporal feature learning and classification, enabling it to effectively capture complex underlying patterns in temporal process data and overcome the limitations of conventional methods. The DTNN's capabilities are demonstrated in the context of micro-electrical discharge machining (micro-EDM), a prominent non-traditional machining process known for producing intricate and high-precision components. Through a pulse discrimination task utilizing a large dataset of reliable labels, the DTNN achieves superior classification accuracy under varying processing parameters while providing interpretable insights into discharge phenomena. Furthermore, the DTNN monitoring approach is applied to a deep-hole drilling process in micro-EDM, enabling closed-loop control of discharge status and ensuring long-term process stability. The DTNN's modular design, interpretability and real-time adaptability underscore its potential for advancing data-driven monitoring systems in digital manufacturing. © 2025 The Society of Manufacturing Engineers

关键词： computer control

来源：评论

学校读者我要写书评

暂无评论

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma

引用

computers in Biology and Medicine 2024年 178卷 108692-108692页

作者： Zhou, Murong Li, Hongfei Gao, Bo Zhao, Yuming College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Life Science Northeast Forestry University Harbin150040 China Department of Radiology The Second Affiliated Hospital Harbin Medical University Harbin150040 China

Background: Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. Method: Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. Results: Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. Conclusions: The seven

关键词： Fibroblasts

来源：评论

学校读者我要写书评

暂无评论

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust Maximum Power Point Tracking in PV Generation System: A Hybrid ANN-Backstepping Approach With PSO-GA Optimization

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Hussan, Umair Waheed, Ahsan Bilal, Hazrat Wang, Huaizhi Hassan, Mudasser Ullah, Inam Peng, Jianchun Hosseinzadeh, Mehdi Shenzhen University College of Mechatronics and the Control Engineering Shenzhen518060 China The University of Faisalabad Department of Electrical Engineering Faisalabad38000 Pakistan University of Tasmania School of Engineering HobartTAS7005 Australia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Macquarie University School of Computing Sydney Australia Duy Tan University School of Computer Science Da Nang Viet Nam

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions. © 1975-2011 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Distributed Optimal Load Frequency control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

引用

2021 European control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

来源：评论

学校读者我要写书评

暂无评论

Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction

引用

computers in Biology and Medicine 2025年 188卷 109749-109749页

作者： Kierner, Slawomir Kierner, Piotr Kucharski, Jacek Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Genetics – Blavatnik Institute Sinclair Lab Harvard Medical School D 77 Avenue Louis Pasteur BostonMA02115 United States Faculty of Electrical Electronic Computer and Control Engineering Lodz University of Technology 18/22 Stefanowskiego St. Lodź90-924 Poland

Background: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML) offering higher prediction accuracy at the expense of decision-making transparency. Purpose: This study investigates the efficacy of various aggregation methods combining the decisions of an AI model and a clinical rule-based (RB) engine in predicting vaccine hesitancy to maximize the effectiveness of patient incentive programs. This is the first study of parallel ensemble of rules and machine learning in clinical context proposing RB confidence-led fusion of ML and RB inference. Methods: A clinical decision system for predicting hesitation to vaccinate is developed based on a differentially private set of longitudinal health records of 974,000 US patients and clinical rules obtained from the present literature. Various approaches based on possibility theory have been explored to maximize classification accuracy, capture and hurdle rates while ensuring trustworthiness in clinical interventions. Results: Our findings reveal that the hybrid approach outperforms the individual models and RB systems when transparency and accuracy are critical. A RB confidence-led approach emerged as the most effective method. The aggregation of mismatched classes relies on RB results when the RB engine has high confidence (expressed as more than the median degree of membership to the vaccination hesitation output function) and on ML predictions when the RB engine exhibits lower confidence. Conclusions: Implementing such an aggregation method preserves the accuracy and capture rates of a clinical decision system, while potentially improving acceptance among healthcare providers. © 2025 The Authors

关键词： Vaccines

来源：评论

学校读者我要写书评

暂无评论

Learning of Nash Equilibria in Risk-Averse Games

arXiv

引用

arXiv 2024年

作者： Wang, Zifan Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology Digital Futures StockholmSE-10044 Sweden The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically, the agents adopt the conditional value at risk (CVaR) as a risk measure with possibly different risk levels. To solve this problem, we propose a first-order risk-averse leaning algorithm, in which the CVaR gradient estimate depends on an estimate of the Value at Risk (VaR) value combined with the gradient of the stochastic cost function. Although estimation of the CVaR gradients using finitely many samples is generally biased, we show that the accumulated error of the CVaR gradient estimates is bounded with high probability. Moreover, assuming that the risk-averse game is strongly monotone, we show that the proposed algorithm converges to the risk-averse Nash equilibrium. We present numerical experiments on a Cournot game example to illustrate the performance of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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：