检索结果-内蒙古大学图书馆

Crossbreeding in random forest

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Nadi, Abolfazl Moradi, Hadi Taheri, Khalil Advanced Robotics and Intelligent Systems Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran11155-4563 Iran SKKU Suwon16419 Korea Republic of

Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems. In this paper, we present a novel approach to deal with this problem in Random Forest (RF) as one of the most powerful ensemble methods. The method is based on crossbreeding of the best tree branches to increase the performance of RF in space and speed while keeping the performance in the classification measures. The proposed approach has been tested on a group of synthetic and real datasets and compared to the standard RF approach. Several evaluations have been conducted to determine the effects of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest. The results show better performance of CRF compared to RF. © 2021, CC BY-NC-ND.

关键词： Learning systems

Medical Image Registration and Its Application in Retinal Images: A Review

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Jing Chi, Xueke Wang, Qiufeng Huang, Kaizhu Wang, Da-Han Liu, Yongge Liu, Cheng-Lin College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

TraSeTR: Track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhao, Zixu Jin, Yueming Heng, Pheng-Ann Department Of Computer Science And Engineering The Chinese University Of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory Of Human-Machine Intelligence-Synergy Systems Shenzhen Institute Of Advanced Technology Chinese Academy Of Sciences China Department Of Computer Science University College London United Kingdom

Surgical instrument segmentation - in general a pixel classification task - is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-level predictions i.e., a set of class-bbox-mask pairs, by decoding query embeddings. Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching. A contrastive query learning strategy is further applied to reshape the query feature space, which greatly alleviates the tracking difficulty caused by large temporal variations. The effectiveness of our method is demonstrated with state-of-the-art instrument type segmentation results on three public datasets, including two RAS benchmarks from EndoVis Challenges and one cataract surgery dataset CaDIS. © 2022, CC BY-NC-SA.

关键词： Surgical equipment

Real-time deep learning design tool for far-field radiation profile

学校读者我要写书评

暂无评论

Photonics Research 2021年第4期9卷 I0024-I0028页

作者： Jinran Qie Erfan Khoram Dianjing Liu Ming Zhou Li Gao Department of Electrical and Systems Engineering Washington UniversitySt LouisMissouri 63130USA Department of Electrical and Computer Engineering University of WisconsinMadisonWisconsin 53706USA Key Laboratory for Organic Electronics&Information Displays(KLOEID) Institute of Advanced Materials(IAM)and School of Materials Science and EngineeringNanjing University of Posts&TelecommunicationsNanjing 210046China

The connection between Maxwell’s equations and artificial neural networks has revolutionized the capability and efficiency of nanophotonic *** a machine learning tool can help designers avoid iterative,time-consuming electromagnetic simulations and even allows long-desired inverse ***,when we move from conventional design methods to machine-learning-based tools,there is a steep learning curve that is not as user-friendly as commercial simulation ***,we introduce a real-time,web-based design tool that uses a trained deep neural network(DNN)for accurate far-field radiation prediction,which shows great potential and convenience for antenna and metasurface *** believe our approach provides a user-friendly,readily accessible deep learning design tool,with significantly reduced difficulty and greatly enhanced *** web-based tool paves the way to present complicated machine learning results in an intuitive *** also can be extended to other nanophotonic designs based on DNNs and replace conventional full-wave simulations with a much simpler interface.

关键词： neural design. replace

Diffusion Self-Distillation for Remote Sensing Scene Classification

学校读者我要写书评

暂无评论

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Hu, Yutao Zhang, Lei Luo, Xiaoyan Cao, Xianbin Southeast University School of Computer Science and Engineering Nanjing210096 China Nanjing210096 China Beihang University School of Astronautics Beijing100191 China National Key Laboratory of High-Efficiency Earth-Space Round-Trip Transportation Technologies China National Key Laboratory of Integrated Air-Ground Navigation Technologies Beijing International Science and Technology Cooperation Base for Seamless Health Information Collection and Intelligent Processing China Beihang University School of Electronics and Information Engineering Beijing100191 China Ministry of Industry and Information Technology of China Key Laboratory of Advanced Technologies for Near Space Information Systems Beijing100804 China

Remote sensing scene classification, a fundamental task in remote image analysis, has obtained rapid progress due to the powerful capabilities of Convolutional Neural Networks (CNNs). Achieving precise classification performance heavily relies on the feature extraction capacity of the network. However, due to the large variation and severe distortion within the images, extracting robust feature representations is necessary but challenging. Self-distillation could enhance the shallow layers by providing stronger gradients and more accurate supervision from deeper layers, thereby promoting the extraction of spatially detailed features. Nonetheless, due to the limited capacity of shallow layers to learn truly valuable knowledge, shallow layer features can be viewed as the noisy version of deep layer features and contain more disruptive factors, which significantly impedes the effectiveness of self-distillation. To address this issue, in this paper, we establish the Diffusion Self-Distillation Network (DSDNet), which incorporates the conditional diffusion denoising model into the self-distillation framework. Specifically, DSDNet filters noise from shallow features through the diffusion denoising process, enabling more precise and accurate distillation between the refined student features and the teacher features. Extensive experiments on four challenging remote sensing datasets emonstrate that the proposed DSDNet achieves significant performance improvements over various backbone networks with negligible increases in parameters, delivering state-of-the-art classification performance. © 1980-2012 IEEE.

关键词： Knowledge transfer

Hybrid-integrated high-performance microwave photonic filter with switchable response

学校读者我要写书评

暂无评论

Photonics Research 2021年第8期9卷 1569-1580页

作者： YUANSHENG TAO HAOWEN SHU XINGJUN WANG MING JIN ZIHAN TAO FENGHE YANG JINGBO SHI JUN QIN State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Frontier Science Center for Nano-optoelectronics Peking UniversityBeijing 100871China Peking University Yangtze Delta Institute of Optoelectronics Nantong 226010China Peng Cheng Laboratory Shenzhen 518055China

The integrated microwave photonic filter(MPF),as a compelling candidate for next-generation radio-frequency(RF)applications,has been widely investigated for ***,most integrated MPFs reported thus far have merely incorporated passive photonic components onto a chip-scale platform,while all necessary active devices are still bulk and *** few attempts to higher photonic integration of MPFs have been executed,the achieved filtering performances are fairly limited,which impedes the pathway to practical ***,we demonstrate,for the first time to our knowledge,an all-integrated MPF combined with high filtering performances,through hybrid integration of an In P chip-based laser and a monolithic silicon photonic circuit consisting of a dual-drive Mach–Zehnder modulator,a high-Q ring resonator,and a *** integrated MPF exhibits a high spectral resolution as narrow as 360 MHz,a wide-frequency tunable range covering the S-band to K-band(3 to 25 GHz),and a large rejection ratio of>40 d ***,the filtering response can be agilely switched between the bandpass and band-stop function with a transient respond time(48μs).Compared with previous MPFs in a similar integration level,the obtained spectral resolution in this work is dramatically improved by nearly one order of magnitude,while the valid frequency tunable range is broadened more than twice,which can satisfy the essential filtering requirements in actual RF *** a paradigm demonstration oriented to real-world scenarios,high-resolution RF filtering of realistic microwave signals aiming for interference rejection and channel selection is *** work points out a feasible route to a miniaturized,high-performance,and cost-effective MPF leveraging hybrid integration approach,thus enabling a range of RF applications from wireless communication to radar toward the higher-frequency region,more compact size,and lower power consumption.

关键词： filter modulator switched

EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

Recent Advancements in Solid State Transformer-based EV Fast Charging Stations

学校读者我要写书评

暂无评论

Recent Advancements in Solid State Transformer-based EV Fast...

International Conference on Power Electronics and Drive systems (PEDS)

作者： Ruvini De Seram Anindita Golder Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech University Oshawa ON Canada Ontario Tech-Automotive Center of Excellence (ACE) Ontario Tech University Oshawa ON Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

Growing deployments of ultra fast charging stations (XFCS) are putting a stress on the existing line-frequency transformer (LFT)-based medium voltage (MV) grid due to the high charging demands of exponentially growing electric vehicles (EV). The conventional LFT occupies a large space in the XFCS installation which introduces challenges in facilitating the XFCS infrastructure. As a result, solid-state transformer (SST)-based XFCS are being developed which can be directly connected to the MV grid and several advantages over LFT-based systems such as compactness, intelligence and availability of the DC link to connect renewable energy sources and storage system can be realized. As such, a detailed analysis of current developments in SST-based XFCS is presented and compared with one another in this paper. The challenges associated with the configurations are examined and direction for future research is identified.

关键词：