检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Yıldız, A. Yarkın Kalayci, Asli Department of Electrical and Computer Engineering Northeastern University BostonMA United States School of Business Worcester Polytechnic Institute WorcesterMA United States

Medical diagnosis is a crucial task in the medical field, in terms of providing accurate classification and respective treatments. Having near-precise decisions based on correct diagnosis can affect a patient’s life itself, and may extremely result in a catastrophe if not classified correctly. Several traditional machine learning (ML), such as support vector machines (SVMs) and logistic regression, and state-of-the-art tabular deep learning (DL) methods, including TabNet and TabTransformer, have been proposed and used over tabular medical datasets. Additionally, due to the superior performances, lower computational costs, and easier optimization over different tasks, ensemble methods have been used in the field more recently. They offer a powerful alternative in terms of providing successful medical decision-making processes in several diagnosis tasks. In this study, we investigated the benefits of ensemble methods, especially the Gradient Boosting Decision Tree (GBDT) algorithms in medical classification tasks over tabular data, focusing on XGBoost, CatBoost, and LightGBM. The experiments demonstrate that GBDT methods outperform traditional ML and deep neural network architectures and have the highest average rank over several benchmark tabular medical diagnosis datasets. Furthermore, they require much less computational power compared to DL models, creating the optimal methodology in terms of high performance and lower complexity. Copyright © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

引用

IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Duncan Soiffer Tuomas Pyorre David Kenney Andrew Teixeira Department of Computer Science and Mathematical Sciences Worcester Polytechnic Institute Worcester MA United States Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA United States Department of Chemical Engineering Worcester Polytechnic Institute Worcester MA United States

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis.

关键词： Costs Incineration computer simulation Thermal decomposition Machine learning Predictive models Stability analysis

来源：评论

学校读者我要写书评

暂无评论

On Rejecting Low Quality Images to Improve Deep Smartphone Wound Assessment

On Rejecting Low Quality Images to Improve Deep Smartphone W...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Apiwat Ditthapron Emmanuel O. Agu Peder Pedersen Clifford Lindsay Bengisu Tulu Diane Strong Computer Science Dept. Worcester Polytechnic Institute (WPI) Worcester MA Electrical and Computer Engineering Dept. Worcester Polytechnic Institute (WPI) Worcester MA Department of Radiology Univ. Massachusetts Medical School Worcester MA Foisie Business School Worcester Polytechnic Institute (WPI) Worcester MA

Automatic assessment of wounds using smartphones can facilitate the tracking of healing progress outside the clinic. Prior work proposed SS-PMG-EfficientNets, a state-of-the-art, multi-scale, convolutional neural network architecture for accurate wound assessment. However, the model was previously only trained and evaluated on high-quality wound images captured in controlled environments. This study systematically evaluated the effects of low-quality images on SS-PMG-EfficientNets, finding that the model's performance and consistency are reduced when input wound images are blurry or captured in adverse lighting (too bright or too dark), two factors previously identified as the most prevalent issues in crowdsourced images. Specifically, SS-PMG-EfficientNet was first trained on three high-quality wound datasets containing images labeled with Photographic Wound Assessment Tool (PWAT) wound healing scores, stages of pressure injury wounds, and wound types, respectively. The model was then evaluated on a test wound dataset that was augmented with real-world image luminance, blur, and image compression effects and found that wound assessment accuracy decreased by up to 27%. To improve model performance, we propose a method for instant image quality assessment on the smartphone, enabling low-quality wound images to be rejected immediately after image capture so that the nurse or patient can be prompted to recapture the image. Our evaluation of the proposed image quality assessment and rejection method showed that it increases average accuracy from 73 % to 87%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unified Safety-Critical Motion Planning for Connected Non-Holonomic Agents Using an Adaptive A* and Hybrid A* Integration

Unified Safety-Critical Motion Planning for Connected Non-Ho...

引用

International Conference on Ubiquitous Robots (UR)

作者： Harin Vashi Sumeet Shanbhag Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

This paper presents a unified approach to safety-critical, multi-agent motion planning for connected autonomous robotic systems, seamlessly integrating the kinematic, dynamic, and safety constraints of individual agents, while reducing computational expense to ensure real-time applicability. By integrating Voronoi Cells with an adaptive blend of A* and Hybrid A* algorithms, the proposed combinational planner ensures the generation of feasible and executable trajectories, guaranteeing efficient and collision-free navigation of multiple agents in dynamically complex environments. An additional deadlock avoidance strategy is proposed to further enhance the safety layer. We demonstrate the effectiveness and robustness of our approach in terms of efficiency, collision avoidance, and deadlock resolution through simulations in diverse, randomly generated environments. The results show that the proposed method outperforms existing methods in terms of dynamic considerations and obstacle avoidance, making it a practical realtime motion planning approach for connected non-holonomic agents in complex environments.

关键词： Navigation Dynamics System recovery Robustness Real-time systems Planning Safety

来源：评论

学校读者我要写书评

暂无评论

Latent Dynamical Model to Characterize Brain Network-Level Rhythmic Dynamics

Latent Dynamical Model to Characterize Brain Network-Level R...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Reza Saadati Fard Navid Ziaei Ali Yousefi Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Characterizing network-level rhythmic dynamics over multiple spatio-temporal scales can significantly advance our understanding of brain cognitive function and information processing. In this research, we propose a new switching state space model called latent dynamical coherence model or briefly LDCM. In the LDCM, we develop model inference and parameter estimation solutions that facilitate studying network-level rhythmic dynamics at scales. In the proposed framework, we incorporate both continuous and discrete state processes, helping us to capture dynamics of functional connectivity at various rates, such as slow, rapid, or a combination of both. We then demonstrate an application of our model in characterizing circuit dynamics of the anesthetic state in a sample data set, recorded from a patient under anesthesia using 64-channel EEG over the course of two hours.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PAAMP: Polytopic Action-Set And Motion Planning for Long Horizon Dynamic Motion Planning via Mixed Integer Linear Programming

PAAMP: Polytopic Action-Set And Motion Planning for Long Hor...

引用

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

作者： Akshay Jaitly Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear characteristics of these problems. We introduce a technique that utilizes learned representations of the system, known as Polytopic Action Sets, to efficiently compute long-horizon trajectories. By employing a suitable sequence of Polytopic Action Sets, we transform the long-horizon dynamically feasible motion planning problem into a Linear Program. This reformulation enables us to address motion planning as a Mixed Integer Linear Program (MILP). We demonstrate the effectiveness of a Polytopic Action-Set and Motion Planning (PAAMP) approach by identifying swing-up motions for a torque-constrained pendulum as fast as 0.75 milliseconds. This approach is well-suited for solving complex motion planning and long-horizon Constraint Satisfaction Problems (CSPs) in dynamic and underactuated systems such as legged and aerial robots.

关键词： Dynamics Optimization methods Transforms Autonomous aerial vehicles Planning Trajectory Mixed integer linear programming Dynamic programming Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Semantic Segmentation for Robotic Apple Harvesting: A Deep Learning Approach Leveraging U-Net, Synthetic Data, and Domain Adaptation

Semantic Segmentation for Robotic Apple Harvesting: A Deep L...

引用

International Conference on Ubiquitous Robots (UR)

作者： Ghokulji Selvaraj Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

This paper introduces a deep learning-based semantic segmentation framework tailored for robotic apple harvesting, leveraging synthetic data generated within a 3D simulated apple orchard. The proposed simulation environment replicates real-world scenarios, encompassing challenges such as occlusion, variety in apple types, and changes in lighting conditions. This approach eliminates the extensive costs and complexities associated with collecting real-world datasets, particularly in unpredictable agricultural settings. The synthetic dataset, rendered from perspectives consistent with a robotic harvester's camera in the Gazebo physics engine, provides a comprehensive range of scenarios for robust model training. For validation, we deploy U-Net, a fully convolutional neural network, emphasizing its adaptability to domain shifts between synthetic and real-world data. By integrating strategies such as domain adaptation, data augmentation, and the inclusion of pre-trained ResNet-50 encoders in the U-Net framework, we demonstrate superior performance in detecting and segmenting apples in diverse real-world conditions compared to standard U-Net models and traditional computer vision techniques. The results highlight the potential of synthetic data in deep learning-based semantic segmentation, offering a cost-effective and scalable solution when real-world data is limited or hard to collect.

关键词： Training Adaptation models Three-dimensional displays Semantic segmentation Robot vision systems Lighting Standards

来源：评论

学校读者我要写书评

暂无评论

Learning-Based Design of Off-Policy Gaussian Controllers: Integrating Model Predictive Control and Gaussian Process Regression

Learning-Based Design of Off-Policy Gaussian Controllers: In...

引用

American Control Conference (ACC)

作者： Shiva Kumar Tekumatla Varun Gampa Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper presents an off-policy Gaussian Predictive Control (GPC) framework aimed at solving optimal control problems with a smaller computational footprint, thereby facilitating real-time applicability while ensuring critical safety considerations. The proposed controller imitates classical control methodologies by modeling the optimization process through a Gaussian process and employs Gaussian Process Regression to learn from the Model Predictive Control (MPC) algorithm. Notably, the Gaussian Process setup does not incorporate a built-in model, enhancing its applicability to a broad range of control problems. We applied this framework experimentally to a differential drive mobile robot, tasking it with trajectory tracking and obstacle avoidance. Leveraging the off-policy aspect, the controller demonstrated adaptability to diverse trajectories and obstacle behaviors. Simulation experiments confirmed the effectiveness of the proposed GPC method, emphasizing its ability to learn the dynamics of optimal control strategies. Consequently, our findings highlight the significant potential of off-policy Gaussian Predictive Control in achieving real-time optimal control for handling of robotic systems in safety-critical scenarios.

关键词： Adaptation models Trajectory tracking Computational modeling Optimal control Gaussian processes Predictive models Real-time systems

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study of Domain Adaptation: Are We Really Learning Transferable Representations?

An Empirical Study of Domain Adaptation: Are We Really Learn...

引用

IEEE International Conference on Big Data

作者： Nicholas Josselyn Biao Yin Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781665480468

Deep learning often relies on the availability of a large amount of high-quality labeled data, which can be very limited in novel domains. To address such data scarcity, domain adaptation is one promising approach that allows for deep networks to leverage large amounts of available data from a source domain to enhance the model’s efficacy on the target domain of interest. However, while there is a plethora of alternate models for domain adaptation proposed over many years in the literature, there is a dearth of studies that objectively compare the relative effectiveness of these models in a rigorous, empirical study. To fill this gap, we provide a thorough, unbiased, empirical study of five state-of-the-art (SOTA) deep domain adaptation models proposed over the past 6 years whose codes are publicly available. Models are evaluated on the complex and diverse domain adaptation tasks featured in the DomainNet benchmark dataset as well as the popular Office-31 dataset. Our results suggest that (1) all 5 models perform similarly, on average, and do not even significantly beat the oldest model, and (2) counter to their intended purpose, the transfer loss functions in the literature do not contribute significantly to learning transferable representations. Our observations suggest that domain adaptation research needs to more thoroughly compare newly proposed models against existing works, along with assessing their loss functions’ utility thoroughly. Our code and data splits are made public for reproducibility of results by the community.

关键词： Deep learning Adaptation models Codes Big Data Benchmark testing Reproducibility of results Data models

来源：评论

学校读者我要写书评

暂无评论

SAFE: Sound Analysis for Fall Event detection using machine learning

引用

Smart Health 2025年 35卷

作者： Garcia, Antony Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester 01609 MA United States Department of Electrical Engineering Technological University of Panama Panama City 0819 Panama

This study evaluates the application of machine learning (ML) and deep learning (DL) algorithms for fall detection using sound signals. The work is supported by the Sound Analysis for Fall Events (SAFE) dataset, comprising 950 audio samples, including 475 fall events recorded with a grappling dummy to simulate realistic scenarios. Decision tree-based ML algorithms achieved a classification accuracy of 93% at lower sampling rates, indicating that critical features are preserved despite reduced resolution. DL models, using spectrogram-based feature extraction, reached accuracies up to 99%, surpassing traditional ML methods in performance. Linear models also achieved high accuracy (up to 97%) in various spectrogram techniques, emphasizing the separability of audio features. These results establish the viability of sound-based fall detection systems as efficient and accurate solutions. © 2024

关键词： Audio classification Deep learning Fall detection Feature extraction Machine learning Spectrogram analysis

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：