检索结果-内蒙古大学图书馆

Improving path planning efficiency for underwater gravity-aided navigation based on a new depth sorting fast search algorithm

引用

Defence technology（防务技术） 2024年第2期32卷 285-296页

作者： Xiaocong Zhou Wei Zheng Zhaowei Li Panlong Wu Yongjin Sun School of Automation Nanjing University of Science and TechnologyNanjing210094China China Academy of Aerospace Science and Innovation Beijing100176China Qian Xuesen Laboratory of Space Technology China Academy of Space TechnologyBeijing100094China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin150001China

This study focuses on the improvement of path planning efficiency for underwater gravity-aided ***,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered ***,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation ***,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,*** results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.

关键词： Depth Sorting Fast Search algorithm Underwater gravity-aided navigation Path planning efficiency Quick Rapidly-exploring Random Trees*(QRRT*)

来源：评论

学校读者我要写书评

暂无评论

Conventional and micro scale finite element modeling for metal cutting process:A review

引用

Chinese Journal of Aeronautics 2024年第2期37卷 199-232页

作者： Le WANG Caixu YUE Xianli LIU Ming LI Yongshi XU Steven Y.LIANG Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlanta 30332USA

The metal cutting process is accompanied by complex stress field,strain field,temperature *** comprehensive effects of process parameters on chip morphology,cutting force,tool wear and residual stress are complex and *** element method(FEM)is considered as an effective method to predict process variables and reveal microscopic physical phenomena in the cutting ***,the finite element(FE)simulation is used to research the conventional and micro scale cutting process,and the differences in the establishment of process variable FE simulation models are distinguished,thereby improving the accuracy of FE *** reliability and effectiveness of FE simulation model largely depend on the accuracy of the simulation method,constitutive model,friction model,damage model in describing mesh element,the dynamic mechanical behavior of materials,the tool-chip-workpiece contact process and the chip formation *** this paper,the FE models of conventional and micro process variables are comprehensively and up-to-date reviewed for different materials and machining *** purpose is to establish a FE model that is more in line with the real cutting conditions,and to provide the possibility for optimizing the cutting process *** development direction of FE simulation of metal cutting process is discussed,which provides guidance for future cutting process modeling.

关键词： Conventional and micro scale Finite element simulation Metal cutting process Micro cutting Modeling

来源：评论

学校读者我要写书评

暂无评论

Revisiting Context Aggregation for Image Matting 41

Revisiting Context Aggregation for Image Matting

引用

41st International Conference on Machine Learning, ICML 2024

作者： Liu, Qinglin Lv, Xiaoqian Meng, Quanling Li, Zonglin Lan, Xiangyuan Yang, Shuo Zhang, Shengping Nie, Liqiang School of Computer Science and Technology Harbin Institute of Technology Weihai China Peng Cheng Laboratory Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Traditional studies emphasize the significance of context information in improving matting performance. Consequently, deep learning-based matting methods delve into designing pooling or affinity-based context aggregation modules to achieve superior results. However, these modules cannot well handle the context scale shift caused by the difference in image size during training and inference, resulting in matting performance degradation. In this paper, we revisit the context aggregation mechanisms of matting networks and find that a basic encoder-decoder network without any context aggregation modules can actually learn more universal context aggregation, thereby achieving higher matting performance compared to existing methods. Building on this insight, we present AEMatter, a matting network that is straightforward yet very effective. AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced axis-wise learning (AEAL) blocks to build a basic network with strong context aggregation learning capability. Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data. Extensive experiments on five popular matting datasets demonstrate that the proposed AEMatter outperforms state-of-the-art matting methods by a large margin. The source code is available at https://***/aipixel/AEMatter. Copyright 2024 by the author(s)

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

引用

Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

来源：评论

学校读者我要写书评

暂无评论

Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform

引用

computers, Materials & Continua 2023年第7期76卷 771-791页

作者： Leilei Li Yansong Wang Dongjie Zhu Xiaofang Li Haiwen Du Yixuan Lu Rongning Qu Russell Higgs Artificial Intelligence Academy Wuxi Vocational College of Science and TechnologyWuxi214068China School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China School of Mathematics and Statistics University College DublinDublinDO4 V1W8Ireland

With the rapid development of Internet technology,the issues of network asset detection and vulnerability warning have become hot topics of concern in the ***,most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks,which cannot meet the current needs of the *** address the above issues,this paper proposes a distributed network asset detection and vulnerability warning platform(Dis-NDVW)based on distributed systems and multiple detection ***,this paper proposes a distributed message sub-scription and publication system based on Zookeeper and Kafka,which endows Dis-NDVW with the ability to parallelly process large-scale ***,Dis-NDVW combines the RangeAssignor,RoundRobinAssignor,and StickyAssignor algorithms to achieve load balancing of task nodes in a distributed detection *** terms of a large-scale task processing strategy,this paper proposes a task partitioning method based on First-In-First-Out(FIFO)*** method realizes the parallel operation of task producers and task consumers by dividing pending tasks into different queues according to task *** ensure the data reliability of the task cluster,Dis-NDVW provides a redundant storage strategy for master-slave partition *** terms of distributed storage,Dis-NDVW utilizes a distributed elastic storage service based on ElasticSearch to achieve distributed storage and efficient retrieval of big *** verification shows that Dis-NDVW can better meet the basic requirements of ultra-large-scale detection tasks.

关键词： Distributed network security network asset detection vulnerability warning

来源：评论

学校读者我要写书评

暂无评论

Correction of the Temperature Effect of Muon Counts Observed at the Guangzhou Station

引用

Research in Astronomy and Astrophysics 2023年第2期23卷 132-139页

作者： Cao-Long Xu Yang Wang Gang Qin Ping-Bing Zuo School of Science Harbin Institute of TechnologyShenzhenShenzhen 518055China Laboratory for Space Weather Storms Institute of Space Science and Applied TechnologyHarbin Institute of TechnologyShenzhenShenzhen 518055China

Muons are the main component of secondary cosmic rays,and the variation in muon intensity indicates the variation in primary cosmic ray ***,before using muons to study the variation in the intensity of cosmic rays,it is necessary to eliminate the atmospheric effects,such as pressure and temperature *** this work,the temperature effect of the muons is corrected in terms of empirical method by using ground *** temperature correction is applied to the muon data observed at the Guangzhou station during the period2010–2021 after a barometric *** is found that the effect of seasonal variations in temperature on muon counts is greatly eliminated in the corrected ***,the muon data are well correlated with the neutron data in comparison,which verifies the reliability of the corrected muon *** results show that the correction of muon data by using ground temperature is an effective method.

关键词： atmospheric effects-Sun activity-(ISM:)cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Drilling process of indexable drill bit based on Coupled Eulerian-Lagrangian method:A simulation study

引用

Chinese Journal of Aeronautics 2024年第11期37卷 477-492页

作者： Desheng HU Caixu YUE Xianli LIU Zhipeng JIANG Yongshi XU Junhui LU Steven Y.LIANG Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlanta 30332USA

42CrMo steel has the characteristics of high strength,high wear resistance,high impact resistance,and fatigue ***,drilling 42CrMo steel has always been a challenging *** indexable drill bit has the advantages of high processing efficiency and low processing cost and has been widely used in the field of aerospace hole *** better understand the machining mechanism of the indexable drill bit,this paper uses the Coupled EulerianLagrangian method(CEL)to simulate the three-dimensional drilling model for the first *** simulation results of the drilling force obtained by the CEL method and Lagrangian method are compared with the experimental *** is verified that the CEL method is easy to converge and can avoid the problem of program interruption caused by mesh distortion,and the CEL simulation value is more consistent with the actual ***,the simulation results of cutting force and blade cutting edge node temperature under different process parameters are *** variation of time domain cutting force,frequency domain cutting force and tool temperature with process parameters are *** study provides a new method for the prediction of cutting performance and the optimization of process parameters of indexable drills.

关键词： 42CrMo steel Coupled EulerianLagrangian method Indexable drill bit Drilling force Process parameters

来源：评论

学校读者我要写书评

暂无评论

BaMBNet:A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

引用

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 878-892页

作者： Pengwei Liang Junjun Jiang Xianming Liu Jiayi Ma School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Electronic Information School Wuhan UniversityWuhan 430072China IEEE

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational *** is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional *** to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant ***,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred *** this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated *** particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right *** on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image ***,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper *** this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred *** quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)*** the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://***/junjun-jiang/BaMBNet.

关键词： Blur kernel convolutional neural networks(CNNs) defocus deblurring dual-pixel(DP)data meta-learning

来源：评论

学校读者我要写书评

暂无评论

Advanced liquid crystal-based switchable optical devices for light protection applications:principles and strategies

引用

Light(science & Applications) 2023年第4期12卷 512-541页

作者： Ruicong Zhang Zhibo Zhang Jiecai Han Lei Yang Jiajun li Zicheng Song Tianyu Wang Jiaqi Zhu National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150080China Research Center of Analysis and Measurement Harbin Institute of TechnologyHarbin 150080China School of Energy Science&Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of EducationHarbin 150080China

With the development of optical technologies,transparent materials that provide protection from light have received considerable attention from *** important channels for external light,windows play a vital role in the regulation of light in buildings,vehicles,and *** is a need for windows with switchable optical properties to prevent or attenuate damage or interference to the human eye and light-sensitive instruments by inappropriate optical *** this context,liquid crystals(LCs),owing to their rich responsiveness and unique optical properties,have been considered among the best candidates for advanced light protection *** this review,we provide an overview of advances in research on LC-based methods for protection against ***,we introduce the characteristics of different light sources and their protection ***,we introduce several classes of light modulation principles based on liquid crystal materials and demonstrate the feasibility of using them for light *** addition,we discuss current light protection strategies based on liquid crystal materials for different ***,we discuss the problems and shortcomings of current *** propose several suggestions for the development of liquid crystal materials in the field of light protection.

关键词： optical crystal liquid

来源：评论

学校读者我要写书评

暂无评论

DP-BERT: A pre-trained deep language model for depression prediction using microarray data

DP-BERT: A pre-trained deep language model for depression pr...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Junyu Zeng, Min Li, Yiming Wang, Fang Zheng, Ruiqing Liu, Jin Guo, Fei Li, Min Central South University School of Computer Science and Engineering Changsha410083 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China

ISBN: (纸本)9798350386226

In recent years, the increasing number of individuals diagnosed with depression and the growing awareness of its impact on modern society have highlighted the significance of accurate depression diagnosis. Microarray data has played a crucial role in uncovering the genetic mechanisms underlying depression. However, existing methods for depression prediction using microarray data often rely on the selection of differentially expressed genes. This approach disregards important information from other genes and is susceptible to batch effects, thereby limiting generalizability and model stability. To address these limitations, we propose DP-BERT, a depression prediction model based on Bidirectional Encoder Representations from Transformers (BERT). DP-BERT follows a pre-training and fine-tuning paradigm, leveraging a large amount of unlabeled microarray data from diverse sequencing platforms for pretraining to extract comprehensive genetic-level representations of psychiatric disorders. Subsequently, supervised fine-tuning is performed for depression prediction. Experimental results demonstrate that the pre-trained model achieves superior performance in depression prediction. The source code can be obtained from https://***/CSUBioGroup/DP-BERT. © 2024 IEEE.

关键词： Prediction models

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：