检索结果-内蒙古大学图书馆

AI and Optical Data sciences IV 2023

作者： Venturini, Francesca Michelucci, Umberto Sperti, Michela Gucciardi, Arnaud Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Lucerne University of Applied Sciences and Arts Computer Science Department Lucerne Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9781510659810

The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232 © 2023 SPIE.

关键词： Olive oil

来源：评论

学校读者我要写书评

暂无评论

CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

引用

arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

CBRAMOD: A CRISS-CROSS BRAIN FOUNDATION MODEL FOR EEG DECODING

arXiv

引用

arXiv 2024年

作者： Wang, Jiquan Zhao, Sha Luo, Zhiling Zhou, Yangxuan Jiang, Haiteng Li, Shijian Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China Alibaba Group China Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University China

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on EEG foundation models. However, these studies still leave challenges: Firstly, most of existing EEG foundation models employ full EEG modeling strategy. It models the spatial and temporal dependencies between all EEG patches together, but ignores that the spatial and temporal dependencies are heterogeneous due to the unique structural characteristics of EEG signals. Secondly, existing EEG foundation models have limited generalizability on a wide range of downstream BCI tasks due to varying formats of EEG data, making it challenging to adapt to. To address these challenges, we propose a novel foundation model called CBraMod. Specifically, we devise a criss-cross transformer as the backbone to thoroughly leverage the structural characteristics of EEG signals, which can model spatial and temporal dependencies separately through two parallel attention mechanisms. And we utilize an asymmetric conditional positional encoding scheme which can encode positional information of EEG patches and be easily adapted to the EEG with diverse formats. CBraMod is pre-trained on a very large corpus of EEG through patch-based masked EEG reconstruction. We evaluate CBraMod on up to 10 downstream BCI tasks (12 public datasets). CBraMod achieves the state-of-the-art performance across the wide range of tasks, proving its strong capability and generalizability. The source code is publicly available at https://***/wjq-learning/CBraMod. Copyright © 2024, The Authors. All rights reserved.

关键词： Noninvasive medical procedures

来源：评论

学校读者我要写书评

暂无评论

Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors Data models Intelligent systems Facial features Principal component analysis Least mean squares methods

来源：评论

学校读者我要写书评

暂无评论

A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements

A Deep-Learning-Based Method for Gaining More Biomechanical ...

引用

2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Wang, Ye Shan, Gongbing Li, Hua Feng, Ruliang Zhang, Yilan Li, Guanglin Wang, Lin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macau Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China University of Lethbridge Department of Mathematics and Computer Science LethbridgeABABT1K3M4 Canada Department of Kinesiology and Physical Education University of Lethbridge LethbridgeABT1K3M4 Canada

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Real-Time biomechanical feedback can provide direct and objective quantified information for any practitioners, such as the athletes/coaches, to assist in accelerating their motor skills' learning and training process. However, it is usually difficult to monitor the human motion in full and acquire the key biomechanical parameters (i.e., the kinematic and kinetic data, the EMG, etc.) just with few sensors in some elite sports involving fast movements and complex motor skills. Using too many sensors in the field tests may limit the athletes' motor ability and affect the collected data's validity and reliability. In this paper, we employ a deep learning method to immensely reduce the number of sensors required for providing the real-Time biomechanical feedback in field, according to the hammer-Throw local motion features found from our pilot study. Based on the Keras API imported from the TensorFlow open-source platform, two Sequential Neural Network models are implemented and compared. One model has two inputs (i.e., vertical displacements and velocities on waist) and six outputs (i.e., vital joint angles on lower limbs). The other one has four inputs (i.e., vertical displacements and velocities on wrist and waist) and thirteen outputs (i.e., vital joint angles on both upper and lower limbs). The experimental results demonstrate that the vital joint angles on the upper and lower limbs have strong correlation with the vertical wrist and waist/hip displacements respectively. This study indicates that fewer wearable sensors can be applied in fast and complex movements to obtain the most significant kinematic data, whereas more biomechanical parameters can be further gained by prediction. © 2022 IEEE.

关键词： Kinematics

来源：评论

学校读者我要写书评

暂无评论

Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

引用

18th Pacific Rim International Conference on Artificial intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Human-centric assembly in smart factories

引用

CIRP Annals 2025年

作者： Wang, Lihui Gao, Robert X. Krüger, Jörg Váncza, József Department of Production Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland United States Institute for Machine Tools and Factory Management Technical University Berlin Germany Laboratory of Engineering and Management Intelligence Institute for Computer Science and Control Hungary

Assembly in future smart factories needs to address three challenges, including human centricity, sustainability, and resilience. Conventional approaches for automation in assembly have reached a bottleneck in terms of operation automomy, leaving various tasks to continued manual labour by human operators. To ease the burden on humans both physically and intellectually, human-centric assembly enhanced by augmented robots, cognitive systems, mixed reality and collaborative intelligence, assisted by thought-driven brain robotic controls, provides a promising solution. Within the context, this keynote provides an in-depth analysis of the state of human-centric assembly and identifies potentially fruitful research directions in future smart factories. © 2025 The Author(s)

关键词： Factory automation

来源：评论

学校读者我要写书评

暂无评论

Novel ground reaction force-based parameters for monitoring rehabilitation in tibial fractures

引用

Gait & Posture 2024年 113卷 256-257页

作者： Christian Wolff Elke Warmerdam Tim Dahmen Tim Pohlemann Philipp Slusallek Bergita Ganse German Research Center for Artificial Intelligence Agents and Simulated Reality Saarbrücken Germany Saarland University Saarbrücken Graduate School of Computer Science Saarbrücken Germany Saarland University Werner Siemens-Endowed Chair for Innovative Implant Development Fracture Healing- Departments and Institutes of Surgery Homburg Germany Hochschule Aalen Chair for Computer Vision and Machine Learning Aalen Germany Saarland University Department of Trauma- Hand and Reconstructive Surgery- Departments and Institutes of Surgery Homburg Germany

来源：评论

学校读者我要写书评

暂无评论

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

引用

IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

来源：评论

学校读者我要写书评

暂无评论

Adaptive Federated Minimax Optimization with Lower Complexities

arXiv

引用

arXiv 2022年

作者： Huang, Feihu Wang, Xinrui Li, Junyi Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve the distributed minimax problems. However, these federated minimax algorithms still suffer from high gradient or communication complexity. Meanwhile, few algorithm focuses on using adaptive learning rate to accelerate these algorithms. To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i.e., AdaFGDA) to solve these distributed minimax problems. Specifically, our AdaFGDA builds on the momentum-based variance reduced and local-SGD techniques, and it can flexibly incorporate various adaptive learning rates by using the unified adaptive matrices. Theoretically, we provide a solid convergence analysis framework for our AdaFGDA algorithm under non-i.i.d. setting. Moreover, we prove our AdaFGDA algorithm obtains a lower gradient (i.e., stochastic first-order oracle, SFO) complexity of O (ϵ-3) with lower communication complexity of O (ϵ-2) in finding ϵ-stationary point of the nonconvex minimax problems. Experimentally, we conduct some experiments on the deep AUC maximization and robust neural network training tasks to verify efficiency of our algorithms. © 2022, CC BY-NC-SA.

关键词： Learning algorithms

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：