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3D Model classification based on regnet design space and voting algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 42391页

作者： Gao, Xueyao Yan, Shaokang Zhang, Chunxiang Harbin Univ Sci & Technol Sch Comp Sci & Technol 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China

3D models are widely used in industrial manufacturing, virtual reality, medical diagnosis and so on. At present, view-based 3D model classification has become an important research topic. However, single view feature can not describe the overall shape of 3D model. When multiple views are fused to describe 3D model, useful information is confused. It causes certain interference to determine 3D model's category. To solve these problems, a novel method of 3D model classification based on RegNet design space and voting algorithm is proposed. Firstly, 2D views of 3D model are input into RegNet design space with attention mechanism to extract high-level semantic feature(HSF). Secondly, HSF and the corresponding low-level shape features (LSF) of view are fused, including D1, D2, D3, Fourier descriptor, and Zernike moment. Thirdly, LSTM is combined with softmax function to extract more representative features from the fused feature. Finally, based on discriminative features, improved voting algorithm based on shannon entropy is constructed to determine 3D model's category. Experimental results show that average accuracy of the proposed method on ModelNet10 reaches 94.93%, and the classification performance is outstanding.

关键词： 3D model classification Semantic feature RegNet design space voting algorithm Shape features

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Expiremental validation of a fault tolerant control of induction motor using a voting algorithm 3

Expiremental validation of a fault tolerant control of induc...

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INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT)

作者： Gouichiche, Abdelmadjid Zabar, Mohamed Chedjara, Zakaria Safa, Ahmed Tadjine, Mohamed Univ Ibn Khaldoun Lab Genie Elect & Plasmas Tiaret Algeria Ecole Natl Polytech Lab Commandes Proc Algiers Algeria

ISBN: (纸本)9781479982134

In this paper, we describe a method for designing an active fault tolerant control system for speed sensor fault of an induction motor. The aim of this work is to develop an algorithm receiving multiple outputs from different version (sensor or observers) to choose between the most probably adequate for such situation (nominal or faulty). This algorithm is called maximum-likelihood voting (MLV) algorithm. Two adaptive observers are used (Luenberger and sliding mode observers). The proposed approach is verified by experiment results.

关键词： fault tolerant control (FTC) induction motor speed sensor failure voting algorithm observers

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Identification by Gait using Convolutional Restricted Boltzmann Machine and voting algorithm 11

Identification by Gait using Convolutional Restricted Boltzm...

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IEEE International Congress on Cybermatics / IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology

作者： Yu, Tao Liu, Xingang Mei, Yongyong Dai, Cheng Yan, Jingren Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538679753

Human identification by biological features has been popular in our daily life. Compared with other identification methods, human gait has specific advantages for distinguishing who is the subject. The existing gait recognition methods mainly adopt the features that designed by the hands, which is a complex feature engineering. In this paper, we propose a new gait recognition architecture based on deep learning and mathematical voting algorithm. Different with the conventional methods, the convolutional restricted Boltzmann machine (CRBM) is adopted to extract features by the unsupervised method, and the fully connected layers are employed to learn the gait feature. Meanwhile, the voting algorithm is added to the architecture by using statistical analysis algorithm to identify subjects. The experimental results show that the proposed architecture could outperform state-of-the-arts on the CASIA-B gait dataset.

关键词： Gait recognition CRBM voting algorithm human identification

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Adaptive Tracking of Nonlinear Switched Systems with Sensor Uncertainties Based on a Weighted Average voting algorithm 19th

Adaptive Tracking of Nonlinear Switched Systems with Sensor ...

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19th Chinese Intelligent Systems Conference

作者： Cheng, Zhiyi Lin, Yan Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266510 Peoples R China

ISBN: (纸本)9789819968497;9789819968473;9789819968466

In this paper, we proposed an adaptive tracking control by output feedback based on a weighted average voting algorithm for a class of switched systems with external disturbances and sensor uncertainties. A modified weighted average voting algorithm is employed to regulate the proportion of information transmitted by sensors in different cases. By incorporating high gain scaling technique, the dynamic gain state observer and linear-like controller compose the closed-loop system are constructed, which ensures all signals are bounded and tracking errors can converge to a residual set that can be made arbitrarily small within sensor failures. Finally, a numerical simulation was given to illustrate the availability of our scheme.

关键词： Nonlinear switched systems voting algorithm Dynamic gain observer Sensor uncertainties

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Prediction of Thermophilic Proteins Using voting algorithm 7th

Prediction of Thermophilic Proteins Using Voting Algorithm

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7th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Li, Jing Zhu, Pengfei Zou, Quan Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

ISBN: (纸本)9783030179380;9783030179373

Thermophilic proteins have widely used in food, medicine, tanning, and oil drilling. By analyzing the protein sequence, the superior structure and properties of the protein sequence are obtained, which is used to efficiently predict the protein species. In this paper, a voting algorithm was designed independently. Protein features and dimensions were extracted and reduced, respectively. Data was predicted by WEKA. Next, the voting algorithm was applied to the data obtained by the above processing. In this experiment, the highest accuracy rate of 93.03% was achieved. This experiment has at least two advantages: First, the voting algorithm was developed independently. Second, any optimization method was not used for this experiment, which prevents over-fitting. Therefore, voting is a very effective strategy for the thermal stability of proteins. The prediction data set used in this paper can be freely downloaded from http://***/similar to lijing/thermo ***.

关键词： Thermophilic proteins voting algorithm Feature selection Machine learning

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Boosting the Semantics Sensitive Satellite Image Retrieval Using a voting algorithm

Boosting the Semantics Sensitive Satellite Image Retrieval U...

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2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(第一届空间数据挖掘与地理知识服务国际学术会议 ICSDM 2011)

作者： Yikun Li Xiaoyuan Dong School of Mathematics Physics and Software Engineering Lanzhou Jiaotong University Mailbox 504 Lanzhou Jiaotong University Lanzhou 730070P.R.China

This paper proposes a semantic inference approach, which utilizes a group of context-sensitive Bayesian networks to infer the semantic concepts based on different regional spatial relationships, i.e. disjoined, bordering, invaded by, surrounded by, near, far, right, left, above and below. Each Bayesian network performs the inference based on one kind of the regional spatial relationships. Finally, a voting algorithm is proposed to combine the group of the Bayesian networks into a more accurate and robust semantic concept classifier. The experiments using IKONOS imagery show that the precision of the proposed voting algorithm is consistently higher than that of the single context-sensitive Bayesian network.

关键词： semantic inference context-sensitive Bayesian network regional spatial relationship voting algorithm semantic concept classifier

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A Novel Weighted voting algorithm based on Neural Networks for Fault Tolerant Systems

A Novel Weighted Voting Algorithm based on Neural Networks f...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Faraneh Zarafshan Gholam Reza Latif-Shabgahi Abbas Karimi Department of Computer and Communication Systems Engineering Faculty of EngineeringUPMMalaysia Department of Computer Engineering Faculty of EngineeringIslamic Azad University-Arak BranchIran Young Researchers Club Islamic Azad University-Arak BranchIran Computer and Electrical Department Faculty of EngineeringPower and Water university of TechnologyTehranIran

voting algorithms are used in a wide area of control systems from real-time and safety-critical control systems to pattern recognition,image processing and human organization systems in order to arbitrating among redundant results of processing in redundant hardware modules or software *** a point of view,voting algorithms can be categorized to agreement-based voters like plurality and majority or some voters which produce output regardless to agreement existence among the results of redundant *** some applications it is necessary to use second type voters including median and weighted *** both of median and weighted average voters are the choicest voters for highly available application,weighted average voting is often more trustable than *** median voter simply selects the mid-value of results;weighted average voter assigns weight to each input,based on their pre-determined priority or their differences,so that the share of more trustable inputs will increase rather than the inputs with low probable *** paper introduces a novel weighted average voting algorithm based on neural networks that is capable of improving the rate of system *** experimental results showed that the neural weighted average voter has increases the reliability 116.63% in general and 309.82%,130.27% and 9.37% respectively for large,medium and small errors in comparison with weighted average,and 73.87% in general and 160.44%,83.59% and 7.52% respectively for large,medium and small errors in comparison with median voter.

关键词： voting algorithm fault tolerance neural networks reliability.

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Boosting the Semantics Sensitive Satellite Image Retrieval Using a voting algorithm

Boosting the Semantics Sensitive Satellite Image Retrieval U...

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2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM 2011)

作者： Yikun Li~1,Xiaoyuan Dong~2 School of Mathematics,Physics and Software Engineering,Lanzhou Jiaotong University Mailbox 504 Lanzhou Jiaotong University,Lanzhou 730070,P.R.China

This paper proposes a semantic inference approach, which utilizes a group of context-sensitive Bayesian networks to infer the semantic concepts based on different regional spatial relationships,***,bordering,invaded by,surrounded by,near,far,right,left,above and *** Bayesian network performs the inference based on one kind of the regional spatial ***,a voting algorithm is proposed to combine the group of the Bayesian networks into a more accurate and robust semantic concept *** experiments using IKONOS imagery show that the precision of the proposed voting algorithm is consistently higher than that of the single context-sensitive Bayesian network.

关键词： semantic inference context-sensitive Bayesian network regional spatial relationship voting algorithm semantic concept classifier

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Three-dimensional shape measurement using improved binary spatio-temporal encoded illumination and voting algorithm

Three-dimensional shape measurement using improved binary sp...

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2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

作者： Xue Kang Li Yong Song Fang Institute of Information Optics Zhejiang Normal University Jinhua 321004 China Institute of Command and Technology PLA Beijing 101416 China

ISBN: (纸本)9781612842899

Some regions of objects will be measured incorrectly or can't be measured in optical 3D measurement system based on coded structured light, due to occlusion, shadow, transfer function of measurement system and noise etc. To obtain 3D data as much as possible and as correctly as possible, we proposed a method using improved binary spatio-temporal encoded illumination and voting algorithm. Firstly, the BSE pattern is improved with a redundancy encoding method. One code is assigned to two adjacent sections and distinguished with their temporal coordinates. The redundancy encoding method provides more cues for code correcting and retrieving. Secondly, symbols are estimated according to four coding cues — code redundancy, continuity of stripes, intensity variation in temporal direction and neighbor symbols in sequence. Finally, a voting algorithm is adopted to obtain final symbols. A plaster model of human head was measured to validate the method. The experimental results reveal that more valid points can be obtained and the reliability of the decoding results is improved.

关键词： coded structured light optical three-dimensional shape measurement binary spatio-temporal encoded illumination redundancy encoding error detection and correction voting algorithm

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A Hybrid History Based Weighted voting algorithm for Ultra-Critical Systems

A Hybrid History Based Weighted Voting Algorithm for Ultra-C...

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International Symposium on Communications and Informaiton Technologies

作者： Ahmed Alahmadi Ben Soh Department of Computer Science and Computer Engineering La Trobe University

ISBN: (纸本)9781467311564

There are numerous fault management techniques that can be used to enhance the reliability of a real-time critical system. Fault masking is one of the significant techniques amid those. It is one of the key approaches to improve or maintain the normal behaviour of a system in the appearance of fault. voting as fault masking method involves the derivation of an output data object from a collection of n input data objects, as prescribed by the requirements and constraints of a voting algorithm. In data fusion, voting is a probable method of combining different data delivered by several sources (e.g. sensors) whose outputs may be mistaken. This paper proposes a new concept of classifying voting algorithms with some new parameters of evaluation. Then using this new concept and some widely known voting algorithms, we propose a hybrid history based weighted voting algorithm. We show that the proposed algorithm gives better and stable performance, in different error ranges, compared to the existing standard voting algorithms.

关键词： Hybrid voting algorithm Fault-Masking Fault Tolerance Critical-Systems

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