检索结果-内蒙古大学图书馆

Distributed and Localized Model Predictive Control - Part II: Theoretical Guarantees

IEEE Transactions on Control of Network Systems 2023年第3期10卷 1113-1123页

作者： Alonso, Carmen Amo Li, Jing Shuang Matni, Nikolai Anderson, James California Institute of Technology Computing and Mathematical Sciences Department PasadenaCA91106 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise: such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced distributed and localized model predictive control (DLMPC) for large-scale linear systems;DLMPC is a scalable closed-loop MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this article, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the system level synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchange is necessary, and computational complexity is independent of the global system size: we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability in the nominal and robust settings. © 2014 IEEE.

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

Basketball Win Percentage Prediction using Ensemble-based Machine Learning 6

Basketball Win Percentage Prediction using Ensemble-based Ma...

引用

6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sikka, Dhruv Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665482714

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Prism: Privacy-Preserving Inter-Site MRI Harmonization via Disentangled Representation Learning 22

Prism: Privacy-Preserving Inter-Site MRI Harmonization via D...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Galada, Sarang Halder, Tanurima Deo, Kunal Krish, Ram P. Jadhav, Kshitij Indian Institute of Technology Trust Lab Bombay India School of Computing and Data Science Sai University Chennai India Koita Centre for Digital Health Indian Institute of Technology Bombay India

ISBN: (纸本)9798331520526

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://***/saranggalada/PRISM. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

引用

2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

来源：评论

学校读者我要写书评

暂无评论

The Conflict Between Explainable and Accountable Decision-Making Algorithms 22

The Conflict Between Explainable and Accountable Decision-Ma...

引用

5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Lima, Gabriel Grgić-Hlača, Nina Jeong, Jin Keun Cha, Meeyoung School of Computing Kaist Data Science Group Korea Republic of Max Planck Institute for Software Systems & Max Planck Institute for Research on Collective Goods Germany Kangwon National University Korea Republic of Data Science Group Ibs & School of Computing Kaist Korea Republic of

ISBN: (纸本)9781450393522

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility. © 2022 ACM.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation of Satellite Imagery Using Optimized U-Net Model 1st

Semantic Segmentation of Satellite Imagery Using Optimized U...

引用

1st International Research Conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Kavipriya, J. Vadivu, G. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9783031823855

The classification of land cover is crucial due to increasing demands and population expansion. Segmentation of the terrain is utilized in environmental monitoring to accurately identify and delineate areas of agriculture, buildings, and water bodies. Satellite imagery is quite valuable for the purpose of segmentation. Deep learning models offer automated and precise segmentation of satellite imagery. The U-Net architecture is widely employed as an encoder-decoder structure for the purpose of segmenting medical and remote sensing images. This study investigates the process of dividing satellite images into segments using an optimized U-Net model. Our model utilizes improved equal weights to calculate loss functions. In addition, we have implemented a dropout layer for each convolutional block in order to decrease the computational time required for feature extraction in the encoder layer of the optimized U-Net model. Our study examines the Dubai dataset, which has six distinct classes. The optimized U-Net model we propose achieves an accuracy of 81.5% and Intersection over Union (IoU) values of 61.7% for segmenting the dataset. These metrics are acquired with a reduced number of epochs, hence decreasing the computational expense. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

引用

1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

An Efficient Classification of Cassava Leaf Disease using VGG 16

An Efficient Classification of Cassava Leaf Disease using VG...

引用

2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Soujanya, A. Cherukuvada, Srikanth Murugan, V. Ramya, Singaraju Agalya, K. Gayathri, S. Ibrahimpatnam India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India CARE College of Engineering Department of Artificial Intelligence and Data Science Thayanur Trichy India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Samayapuram Trichy India

ISBN: (纸本)9798350319200

Cassava is an essential food item in the tropics, it may origin enormous productivity decreases in cassava, leading to significant financial losses and a shortage of basic foods. Small-scale farmers grow the essential food and nutrition crop cassava because it can withstand harsh conditions. In many African nations, it provides a substantial source of carbohydrates. Leaf infections can occasionally harm cassava crops, limiting total productivity and farmers' income. In this study, Pre trained models are utilized for the determination of disease identification and classification on plant leaf issues. VGG 16 has been put into use, and its performance has been examined. An exploratory study is carried out using an online Cassava image collection, which contains pictures of Cassava leaves afflicted with five different disease categories. The vgg16 classifier that was suggested surpasses its competitors greatly and achieves an accuracy of 97.58%. The results of the test demonstrate that improving classification performance by using a database of images that is well balanced is possible. © 2023 IEEE.

关键词： Classification CNN Deep learning Transfer learning VGG16

来源：评论

学校读者我要写书评

暂无评论

Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks 4

Traffic Sign Detection in the Digital Era: Leveraging Convol...

引用

4th IEEE International Multidisciplinary Conference on Engineering Technology, IMCET 2023

作者： Kothai, G. Anbumozhi, Anna Abirami, R. Kavin, R.A. Karthikeyan, M. Kalyani, T. Gokulpandian, M. TamilNadu Coimbatore India SRM Institute of Science and Technology Department of Data Science and Business TamilNadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies TamilNadu Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Virudhunagar India

ISBN: (纸本)9798350313826

Road safety depends heavily on traffic signs, and as there are more vehicles on the road, the demand for precise traffic sign recognition systems rises. The development of machine-learning algorithms to predict and recognize traffic signs in real-time has been the main focus of research. An overview of the state of advancement in image analysis and classification for recognizing road signs is given in this article. It also goes through their benefits and drawbacks in various traffic situations. Managing various lighting and weather situations, the necessity for real-time processing, and future connection with autonomous driving systems are just a few of the difficulties that are highlighted in the paper. This study seeks to provide guidance to professionals working in the areas of autonomous driving, computer vision, and machine learning who recognize traffic signs. © 2023 IEEE.

关键词： Traffic signs

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：