2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)最新文献_第9页

Real-time fuzzy logic-based sport activity risk calculation model optimization 基于实时模糊逻辑的体育活动风险计算模型优化

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705209

E. Tóth-Laufer, M. Takács, I. Rudas

引用次数: 6

BINESA — A software tool for evolution modelling of biochemical networks' structure BINESA -生物化学网络结构的进化建模软件工具

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705218

T. Rubina, E. Stalidzans

{"title":"BINESA — A software tool for evolution modelling of biochemical networks' structure","authors":"T. Rubina, E. Stalidzans","doi":"10.1109/CINTI.2013.6705218","DOIUrl":"https://doi.org/10.1109/CINTI.2013.6705218","url":null,"abstract":"Biochemical networks of different types such as transcription regulatory, gene regulation, metabolic, protein interaction and signal transduction networks have been extensively studied. One of research directions is related with topological properties of biochemical networks and their structure evolution. Earlier research clearly shows that biochemical networks differ considerably from random ones in their network structure. The reason could be mutation and selection based evolution combined with different importance of particular paths of the network for the survival of an organism. It would be useful to investigate and estimate the changes of network structure that are caused by different types of mutation during the evolution, such as point mutations, missense, nonsense mutations, nucleotide inversion, gene inversion, duplication, deletion, and translocation mutations. BINESA (BIochemical NEtwork Structure Analyser) is a standalone software tool developed for evolution modelling of a biochemical network structure founded on evolutionary changes of an underlying artificial genome. This software tool is written in Visual Basic programming language and developed for Windows operating system using Microsoft Access. BINESA has a database for storing of network structure and artificial genome data. This tool provides the exploration of the evolution dynamics of biochemical network structure, supports SBML and GML models import and export, allows visualization of the network structure in graph form visually marking out reactions of different importance and perform a topological analysis of the biochemical network structure.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130984865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Time domain passivity based teleoperation applying bounded output controllers 应用有界输出控制器的时域无源遥操作

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705170

Lorinc Martori

引用次数: 1

Adaptable fuzzy control solutions for driving systems working under continuously variable conditions 连续变工况下驱动系统的自适应模糊控制方法

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705198

A. Stînean, S. Preitl, R. Precup, C. Dragos, M. Radac, Marius-Florin Crainic

引用次数: 2

Model of human clothes based on saliency maps 基于显著性地图的人体服装模型

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705258

S. Hommel, Darius Malysiak, U. Handmann

引用次数: 5

3D image quality estimation (ANN) based on depth/disparity and 2D metrics 基于深度/视差和二维度量的三维图像质量估计

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705177

Dragan D. Kukolj, D. Dordevic, David Okolišan, I. Ostojic, Dragana D. Sandić-Stanković, C. Hewage

{"title":"3D image quality estimation (ANN) based on depth/disparity and 2D metrics","authors":"Dragan D. Kukolj, D. Dordevic, David Okolišan, I. Ostojic, Dragana D. Sandić-Stanković, C. Hewage","doi":"10.1109/CINTI.2013.6705177","DOIUrl":"https://doi.org/10.1109/CINTI.2013.6705177","url":null,"abstract":"Immersive image/video services will be soon available to the mass market due to the technological advancement of 3D video technologies, which include 3D-Ready TV monitors at affordable prices. However, in order to provide demanding customers with a better service over resource limited (e.g., bandwidth) and unreliable communication channels, system parameters need to be changed “on the fly”. Measured 3D video quality can be used as feedback information to fine tune the system parameters. The main aim of this paper is to analyze and present impact of objective image quality assessment metrics on perception of 3D image/video. Neural Network statistical estimator was used to examine the correlation between objective measures on input image base and Differential Mean Opinion Score (DMOS) of used image base. For this purpose part of LIVE 3D Image Quality Database [7] was used. The results suggest that comparison of the neural network DMOS estimators based on full-reference and no-reference objective metrics shown very similar behavior and accuracy.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

On additivity of the weighted possibilistic mean operator 关于加权可能性平均算子的可加性

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705211

Lucian C. Coroianu, R. Fullér

引用次数: 3

Solving multiprocessor scheduling problem using multi-objective mean field annealing 用多目标平均场退火法求解多处理机调度问题

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705174

Nasser Lotfi, A. Acan

引用次数: 4

Emotion modeling using Fuzzy Cognitive Maps 基于模糊认知地图的情绪建模

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705252

Hasan Murat Akinci, E. Yesil

引用次数: 14

A supervised spiking time dependant plasticity network based on memristors 基于忆阻器的有监督峰值时间相关的可塑性网络

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI) Pub Date : 2013-11-01 DOI: 10.1109/CINTI.2013.6705238

Xiao Yang, Wanlong Chen, Frank Z. Wang

{"title":"A supervised spiking time dependant plasticity network based on memristors","authors":"Xiao Yang, Wanlong Chen, Frank Z. Wang","doi":"10.1109/CINTI.2013.6705238","DOIUrl":"https://doi.org/10.1109/CINTI.2013.6705238","url":null,"abstract":"Synaptic plasticity has been widely assumed to be the mechanism behind memory and learning, in which, synapse has a critical role. As a newer biologic update rule to hebbian learning, spiking-time dependent plasticity (STDP) concerns on the temporal order of presynaptic spike and postsynaptic spike which will change the strength of, the connection site of neurons, synapse. In this paper a different way is shown to utilise the novel element memristors to implement a supervised STDP. Because the resistance of memristor depends on its past states, researchers are particularly interested in using such functionality to mimic synaptic connection. Furthermore, benefit from the nano size of memristors and its crossbar structure, large scale neural networks could be implemented. In this supervised STDP, each spike arrival will be assumed to leave a trace which decays exponentially and spikes interact under all-to-all interaction. Depending on the temporal order, memristor synapse will weaken or strengthen the connection of presynaptic neuron and postsynaptic neuron. The temporal all-to-all interaction is implemented during the simulation with training samples. We show that, by combining the memristors, a supervised STDP neural network can be built and learn from the temporal order of presynaptic spike and postsynaptic spike of the training samples.","PeriodicalId":439949,"journal":{"name":"2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126560273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9