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Multi-scale pulmonary nodule classification with deep feature fusion via residual network

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14829-14840页

作者： Zhang, Guokai Zhu, Dandan Liu, Xiao Chen, Mingle Itti, Laurent Luo, Ye Lu, Jianwei School of Software Engineering Tongji University Shanghai China High School Affiliated to Fudan University Shanghai China Department of Computer Science and Neuroscience Program University of Southern California Los Angeles United States Institute of Translational Medicine Tongji University Shanghai China

The early stage detection of benign and malignant pulmonary nodules plays an important role in clinical diagnosis. The malignancy risk assessment is usually used to guide the doctor in identifying the cancer stage and making follow-up prognosis plan. However, due to the variance of nodules on size, shape, and location, it has been a big challenge to classify the nodules in computer aided diagnosis system. In this paper, we design a novel model based on convolution neural network to achieve automatic pulmonary nodule malignancy classification. By using our model, the multi-scale features are extracted through the multi-convolution process, and the structure of residual blocks allows the network to capture more high-level and semantic information. Moreover, a strategy is proposed to fuse the features from the last avg-pooling layer and the ones from the last residual block to further enhance the performance of our model. Experimental results on the public Lung Image Database Consortium dataset demonstrate that our model can achieve a lung nodule classification accuracy of 87.5 % which outperforms state-of-the-art methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Expanding the Surgical Robotics Community: An Intuitive Sim-to-Real Control Framework for Raven-II with a Budget-Friendly Gamepad Controller

Expanding the Surgical Robotics Community: An Intuitive Sim-...

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International Symposium on Medical Robotics (ISMR)

作者： Mai Bui Natalie Chalfant Cuiling Sun Sean Fabrega Haonan Peng Kevin Huang Yun-Hsuan Su Department of Computer Science Mount Holyoke College South Hadley MA Department of Electrical and Computer Engineering University of Washington Seattle WA Picker Engineering Program Smith College Northampton MA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

This paper proposes a low-cost interface and refined digital twin for the Raven-II surgical robot. Previous simulations of the Raven-II, e.g. via the Asynchronous Multibody Framework (AMBF), presented salient drawbacks, including control inputs inconsistent with Raven-II software, and lack of stable, high-fidelity physical contact simulations. This work bridges both of these gaps, both (1) enabling robust, simulated contact mechanics for dynamic physical interactions with the Raven-II, and (2) developing a universal input format for both simulated and physical platforms. The method furthermore proposes a low cost, commodity game-controller interface for controlling both virtual and real realizations of Raven-II, thus greatly reducing the barrier to access for Raven-II research and collaboration. Overall, this work aims to eliminate the inconsistencies between simulated and real representations of the Raven-II. Such a development can expand the reach of surgical robotics research. Namely, providing end-to-end transparency between the simulated AMBF and physical Raven-II platforms enables a software testbed previously unavailable, e.g. for training real surgeons, for creating digital synthetic datasets, or for prototyping novel architectures like shared control strategies. Experiments validate this transparency by comparing joint trajectories between digital twin and physical testbed given identical inputs. This work may be extended and incorporated into recent efforts in developing modular or common software infrastructures for both simulation and control of real robotic devices, such as the Collaborative Robotics Toolkit (CRTK).

关键词： Training Medical robotics Costs Collaboration Surgery computer architecture Software

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Introducing spectral attention for long-range dependency in time series forecasting 24

Introducing spectral attention for long-range dependency in ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Bong Gyun Kang Dongjun Lee HyunGi Kim DoHyun Chung Sungroh Yoon Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Electrical and Computer Engineering Seoul National University Department of Future Automotive Mobility Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University and Department of Electrical and Computer Engineering Seoul National University

ISBN: (纸本)9798331314385

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture longrange dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results.

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In situ Observation of Lithium Stripping and Plating Process in an Open-cell All-solid-state Lithium Metal Battery

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1287-1288页

作者： Wu, C. Zhao, L. Ai, Q. Risal, S. Lou, J. Yao, Y. Fan, Zheng Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Materials Science and Nano Engineering Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

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Gauge Length Optimization of das using Fiber Bragg Grating Strain Measurements

Gauge Length Optimization of das using Fiber Bragg Grating S...

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Bragg Gratings, Photosensitivity and Poling in Glass Waveguides and Materials, BGPP 2022

作者： Weber, Guilherme Heim Jahnert, Frederico Gomes, Danilo Fernandes Martelli, Cicero Dreyer, Uilian José Da Silva, Jean Carlos Cardozo Pipa, Daniel Rodrigues De Camargo, Sérgio Taveira Da Silva, Manoel Feliciano Da Silva, Marco José Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal Do Paraná Curitiba80230-901 Brazil Petrobras Research and Development Program Rio de Janeiro21941-970 Brazil

The gauge length parameter selection affects the quality of data measured in Distributed Acoustic Sensing. This paper uses Fiber Bragg Grating's strain measurements in controlled experiments to promote optimizatio... 详细信息

ISBN: (纸本)9781957171166

关键词： Strain measurement

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Transfer Learning-based Lane Line Detection System for Visual Path Following Control

Transfer Learning-based Lane Line Detection System for Visua...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Allan S. Almeida Tiago T. Ribeiro Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

One of the most important tasks in autonomous driving and autonomous vehicle navigation is detecting a path or trajectory that the vehicle should follow. Over the past few years, some learning-based works have stood out more than traditional computer vision techniques in detecting such lanes. In this paper we present an approach to solve the lane line detection problem in the context of visual path following by using a residual factorized convolutional neural network. Experimental results show a promising model that can detect lane lines even under severe lighting conditions and in the presence of occlusions and shadows. The path detection system was tested along with a visual path following formulation based on Nonlinear Model Predictive Control. Still, it can be used for any controller in the context of visual navigation for autonomous vehicles. Nonetheless, the proposed model architecture strikes a remarkable balance between accuracy and efficiency, making the system suitable for real-time applications.

关键词： Measurement Visualization Accuracy Navigation Computational modeling Lighting computer architecture Trajectory Convolutional neural networks Autonomous vehicles

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Effects of the Window Size Variation in the RMS Voltage and Industrial Control Measures

Effects of the Window Size Variation in the RMS Voltage and ...

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Innovative Smart Grid Technologies (ISGT)

作者： Christopher R. Men Douglas E. Dow Umair Zia Afsaneh Ghanavati Electrical and Computer Engineering Program School of Enginnering Wentworth Institue of Technology Boston MA Director of Distribution Engineering Eversource Energy Boston MA

Sags and swells are types of power quality disturbances that involve fluctuations in voltage. Sags are decreases in a voltage’s magnitude while swells are increases in a voltage’s magnitude. There are a number of different techniques to identify and classify these power quality disturbances, however in an industrial setting power utilities typically employ the use of the CBEMA and ITIC curves to classify the power quality disturbances they encounter. The CBEMA and ITIC curves utilize RMS voltage to denote whether a disturbance is expected to impact the system it occurs on, however there are a number of ambiguities surrounding the use of both curves. One such ambiguity is the lack of specification surrounding the window size used to calculate RMS values. This paper proposes MATLAB scripts that can synthesize waveforms with sags and swells according to a variety of user-defined parameters and provide their RMS voltages. This paper also presents a MATLAB function that can plot the RMS voltage magnitude and durations of transient events onto the ITIC curve. This paper explores the effect of varying the window size used to calculate RMS voltage for each disturbance across different parameters. Moreover, the paper presents an analysis of the RMS voltage of a real-world voltage sag in a three-phase system.

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Optimization of Certainty Factor Algorithm to Overcome Uncertainty in Expert System Identification of Pests and Diseases of Cocoa 2

Optimization of Certainty Factor Algorithm to Overcome Uncer...

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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Gunawan, Rakhmat Dedi Ahmad, Imam Parjito Anggono, Harry Vernando, Ego Hengki Jaya, Firman Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Stkip Pgri Information Technology Education Study Program Situbondo Indonesia

ISBN: (纸本)9781665465410

Lampung Province is one of the regions with the highest cocoa production, but many factors can interfere with cocoa production. One of them is the factor of pests and diseases of cocoa that cannot be identified and prevented beforehand. An expert system is a system that adopts expert knowledge or experts in a particular field. However, the diagnosis usually contains uncertainty in the form of answers and statements used. Certainty Factor (CF) algorithm, is one of the algorithms that is able to overcome uncertainty by providing a value for the level of trust from experts and users. However, because the CF value is based on the level of confidence in the symptoms experienced by the user, it creates a problem, namely the consistency of the user's answers. So, it is necessary to make improvements to the CF algorithm, to overcome the inconsistency of user answers through the use of Consistency Ratio (CR). Through the use of CR, inconsistencies are overcome through comparisons between the consistency index and the random index. Based on the results of the accuracy test, the proposed model is able to produce an accuracy rate of 86.67% in the diagnosis of pests and diseases on cocoa plants. © 2022 IEEE.

关键词： Cocoa

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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