检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiao Tian Dong Sui Weifeng Liu Maozu Guo Gongning Luo Kuanquan Wang Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (数字)9798350337488

ISBN: (纸本)9798350337495

Neoadjuvant chemoradiotherapy (nCRT) is the stan-dard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2 % ~ 6% improvement respectively.

关键词： Protocols Magnetic resonance imaging Decision making Cancer treatment Reliability Bioinformatics Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A new method for predicting plant proteins function based on multi label classification algorithm

A new method for predicting plant proteins function based on...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Chen, Shuai Wang, Juan Guo, Maozu Inner Mongolia University School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

ISBN: (纸本)9781665468190

Protein function annotation is an important content of bioinformatics. It is unrealistic to experimentally annotate a large number of protein functions, so automated prediction of the functions of proteins is required. Studying plant proteins can help us cultivate new plant varieties. However, the existing methods mainly aim at predicting the single function of plant proteins. Most proteins only have sequence information. Therefore, the multi-function prediction method of plant protein based on sequence has become the focus of research. In this study, we propose a method based on sequence to predict the function of plant proteins, called PlantGO. We describe the functional annotation problem as a multi-label classification problem using Gene Ontology (GO) terminology, and predict multiple functions for plant proteins. PlantGO extracts features from three aspects and performs feature fusion respectively. To avoid redundant information in features, PlantGO uses the random forest to select features and obtains three groups of optimal features. PlantGO uses a multi-label learning algorithm MLKNN to predict protein function, which proves the effectiveness and accuracy of the algorithm. Finally, PlantGO adopts the ensemble learning method to integrate all models into a unified model, further improving the prediction performance. Therefore, PlantGO has achieved excellent performance. The performance metrics are Accuracy (0.936), F1 Score (0.944), Ranking loss (0.011), which is better than the previous method in the independent test. We have developed an online server to predict the functions of plant proteins. © 2022 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

引用

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

来源：评论

学校读者我要写书评

暂无评论

Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Xiaoting Wang, Juan Bian, Haodong Guo, Maozu Inner Mongolia University Ecological Big Data Engineering Research Center of the Ministry of Education School of Computer Science Hohhot China Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9781665468190

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins. © 2022 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

引用

China Communications 2021年第7期18卷 134-146页

作者： Chao Pan Zhao Wang Zhenyu Zhou Xincheng Ren Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power UniversityBeijing102206Baoding071003HebeiChina Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yanan UniversityYanan 716000China

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality *** this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess ***,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort *** with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

关键词： collaborative vehicular networks task of-floading URLLC awareness deep Q-learning

来源：评论

学校读者我要写书评

暂无评论

Research on Pedestrian Gender Detection Based on A Lightweight Model

Research on Pedestrian Gender Detection Based on A Lightweig...

引用

Chinese Control and Decision Conference, CCDC

作者： Hu Yuling Wang Xinyi Zou Weiguang Institute of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies.

关键词： Pedestrians Accuracy Attention mechanisms Filtering Buildings Urban areas Object detection

来源：评论

学校读者我要写书评

暂无评论

Integrated Building Information Modelling and 3D Printing for Prefabricated Building

Integrated Building Information Modelling and 3D Printing fo...

引用

Chinese Control and Decision Conference, CCDC

作者： Yukang Wang Yu Zhou Xiaoping Zhou Junjun Han Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China Beijing Pi-Innovation Technology Ltd Beijing

The prefabricated components can be connected to form buildings in prefabricated building based on the design drawings. The production of components in the factory is not standardized and accurate, which leads to difficulty in connecting prefabricated components and low efficiency in construction. Excessive construction materials are piled up on the construction site and these wastes have caused a lot of pollution to the environment. Study proposes to integrating Building Information Modelling (BIM) and 3D printing for prefabricated buildings. Triangulation of BIM models is discussed. Prefabricated components information is extracted by Industry Foundation Class (IFC) files so that BIM can provide prefabricated component models. Additionally, the conversion relationship between BIM and 3D printing is established. Thirdly, the conversion rules and methods for converting BIM data into STL files are developed so that the 3D printer can print the prefabricated components according to the corresponding path. Finally, testing the data format conversion on BIM models shows that our presented approach enables us to convert a BIM model into a data format recognizable for 3D printing. Applying BIM and 3D print in prefabricated buildings will boost the fineness of components and the efficiency of construction and have good performance in decreasing engineering waste.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption Prediction of Public Buildings Based on MEA-BP Neural Network 33

Energy Consumption Prediction of Public Buildings Based on M...

引用

第33届中国控制与决策会议

作者： Shuai Wang Hongyan Ma Jiaming Dou Yongxue Ye Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

ISBN: (纸本)9781665431293

data mining technology is often adopted in construction engineering to provide relevant information and reference basis for public building energy consumption *** paper proposes an optimization algorithm applying back propagation(BP) neural network and mind evolutionary algorithm(MEA) to establish a model to predict building energy *** main weather data are used as inputs to the BP neural network and daily electricity consumption is used as an *** MEA is employed to optimise the structure of the network and to predict the energy consumption of *** results demonstrate that the optimized BP neural network has higher fitting accuracy,and the predicted load value is more consistent with the actual value.

关键词： Building energy consumption BP neural network Mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Low-rank matrix recovery with total generalized variation for defending adversarial examples

引用

Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

来源：评论

学校读者我要写书评

暂无评论

Detent Force Reduction in Permanent Magnet Synchronous Linear Motor Based on Arc-teeth

Detent Force Reduction in Permanent Magnet Synchronous Linea...

引用

Chinese Control and Decision Conference, CCDC

作者： Yuxin Shen Rong Guo Hongyan Ma School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Permanent Magnet Synchronous Linear Motors (PMSLM) has the advantages of high thrust force density, high acceleration, high speed, high precision, high efficiency etc. However, the detent force of PMSLM is an inevitable problem, caused by their inherent end-disconnection structure. Excessive detent force will cause thrust fluctuations and reduce the system’s stability. To resolve the above problems, an improved PMSLM structure based on arc-teeth is proposed to reduce detent force. Firstly, the motor permeability distribution is analyzed at different moments based on the flux-tube method, and the expression of detent force is derived. Second, the finite element model of PMSLM is established to optimize the height of arc-teeth, and the optimal structural parameters of the motor is obtained. Finally, the performance advantages of the proposed PMSLM are verified by comparing with traditional linear motor.

关键词： Fluctuations Shape Force Stators Motors Harmonic analysis Permanent magnets

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：