发布求助
文献互助 智能选刊 最新文献

12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)最新文献

筛选
英文 中文
Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images 腹部磁共振图像上皮下和内脏脂肪组织体积的估计
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011511
A. Spasojević, O. Stojanov, T. L. Turukalo, O. Sveljo
{"title":"Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images","authors":"A. Spasojević, O. Stojanov, T. L. Turukalo, O. Sveljo","doi":"10.1109/NEUREL.2014.7011511","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011511","url":null,"abstract":"Fat depots at different location are associated with variable metabolic risks. It has been noted that visceral abdominal adipose tissue contributes more to these risks than subcutaneous adipose tissue. For discrimination between subcutaneous and visceral adipose tissue contemporary studies primarily use cross sectional medical imaging. Fat volume at different anatomical locations is usually identified and determined either manually or in semiautomatic manner. In this study we combined different image processing methods for unsupervised discrimination of subcutaneous and visceral adipose tissue on abdominal T1 MR images. Procedure has been tested on 16 subjects and results are compared with visceral and subcutaneous volume obtained by semiautomatic method from the literature. High correlation was achieved for subcutaneous fat tissue volume (0.98) while for visceral fat tissue good correlation has been noted (0.86).","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123422104","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: 2
Computer aided analysis of projective tests 投影试验的计算机辅助分析
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011481
D. Lacrama, F. A. Pintea, T. M. Karnyanszky, D. S. Codat
{"title":"Computer aided analysis of projective tests","authors":"D. Lacrama, F. A. Pintea, T. M. Karnyanszky, D. S. Codat","doi":"10.1109/NEUREL.2014.7011481","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011481","url":null,"abstract":"This paper is focused on the use of a computer aided method for analyzing the drawings of patients' undergoing projective psychological tests. The sketches are drawn on a standard A4 paper placed over a specially designed touch screen. The authors developed the software aimed to automatically both to automatically measure the geometrical characteristics of the designed object and to deliver some additional parameters (e.g. pen pressure, speed etc.) useful for the clinical study.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122963054","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: 1
Assessment of blast induced ground vibrations by artificial neural network 用人工神经网络评价爆炸引起的地面振动
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011458
S. Kostić, N. Vasovic, I. Franović, A. Samčović, K. Todorović
{"title":"Assessment of blast induced ground vibrations by artificial neural network","authors":"S. Kostić, N. Vasovic, I. Franović, A. Samčović, K. Todorović","doi":"10.1109/NEUREL.2014.7011458","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011458","url":null,"abstract":"Blast-induced ground motion is analyzed by means of two prediction methods. First conventional approach assumes several types of nonlinear dependence of peak particle velocity on scaled distance from the explosion charge, while the second technique implements a feed-forward three-layer back-propagation neural network with three nodes in input layer (total charge, maximum charge per delay and distance from explosive charge to monitoring point) and only one node in output layer (peak particle velocity). As a result, traditional predictors give acceptable prediction accuracy (r>0.7) when compared with registered values of peak particle velocity. Regarding the forecasting accuracy estimated by neural network, model with nine hidden nodes gives reasonable predictive precision (r>0.9), with much lower standard error in comparison to conventional predictors.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314904","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
Fuzzy data in traditional relational databases 传统关系数据库中的模糊数据
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011504
M. Hudec
{"title":"Fuzzy data in traditional relational databases","authors":"M. Hudec","doi":"10.1109/NEUREL.2014.7011504","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011504","url":null,"abstract":"The values of attributes are not always known with sufficient precision to justify the use of traditional databases to store these data. Fuzzy databases have been developed for purpose of managing fuzzy data. They are sophisticated but also complicated for smaller solutions. Our objective is to investigate managing (storing, updating and retrieving) fuzzy data in the traditional relational databases. Furthermore, fuzzy inference systems consist of fuzzy sets and rules. Therefore, we could store these parameters in the same relational database using the same procedures. Hence, the promising research topic could be the integration of information system capable to manage fuzzy data with a self-developed fuzzy inference system.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126403878","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: 4
Gait phase detection optimization based on variational bayesian inference of feedback sensor signal 基于反馈传感器信号变分贝叶斯推理的步态相位检测优化
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011499
N. Malešević, J. Malešević, T. Keller
{"title":"Gait phase detection optimization based on variational bayesian inference of feedback sensor signal","authors":"N. Malešević, J. Malešević, T. Keller","doi":"10.1109/NEUREL.2014.7011499","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011499","url":null,"abstract":"Stroke patients often suffer from gait disorders which can remain chronic. Mechanical or electrical aids designed to deal with this problem often rely on accurate estimation of current gait phase as this information is used for active ankle joint control. In this paper we present the method for optimization of the gait phase detection algorithm. The method is based on Variational Bayesian inference which is employed on signals from feedback sensors positioned on both paretic and healthy foot of patient. Main aim of Variational Bayesian inference application was to remove noise and provide smooth sensor signal which is suitable for robust gait phase detection algorithm. We modeled foot trajectory with linear model. Results presented in this paper show significant reduction of high frequency noise in gyroscope signal. The reduction was dominant during transitions between gait phases making our method applicable in any algorithm based on signal features in time domain.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121037674","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: 7
The fourth element or the missing memristor 第四个元件或缺失的忆阻器
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011440
V. Mladenov
{"title":"The fourth element or the missing memristor","authors":"V. Mladenov","doi":"10.1109/NEUREL.2014.7011440","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011440","url":null,"abstract":"In 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor). Although he showed that such an element has many interesting and valuable circuit properties, until 2008 no one has presented either a useful physical model or an example of a memristor. In the paper in Nature (2008) the team of Stan Williams show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current-voltage behavior observed in many nanoscale electronic that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches. In the talk a brief overview of the memristors will be given and the potential applications will be presented. A promising application of memristor is based on its property to imitate natural nerves. Some research groups use such memristors as key components in a blueprint for an artificial brain. A memristor that is capable of learning will be considered at the end of the talk as well.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121228314","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: 0
EMG based biofeedback with the smarting system 基于肌电图的生物反馈与疼痛系统
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011494
Ilija M. Jovanov, D. Popović
{"title":"EMG based biofeedback with the smarting system","authors":"Ilija M. Jovanov, D. Popović","doi":"10.1109/NEUREL.2014.7011494","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011494","url":null,"abstract":"Learning of a skill is a practice (task related exercise) in which feedback provides information about the performance. If the feedback signal comes from physiological activity then it is termed “biofeedback”. We present a new algorithm for real time classification of muscle activities from several muscles that can be used for feedback that is motivating for the student to learn. We used the “Smarting” system that is light (40 g), self-standing, has a 24-channels digital amplifier, and communicates via Bluetooth with an Android or Windows based platform/monitor. The Smarting system can record voltages above about 1 μV in the frequency range from 0 to 250 Hz (sampling rate at 500 Hz). The algorithm operates on the receiving platform in the Matlab environment. We present implementation of the algorithm for the recognition/distinction of four movements: fingers extension and flexion, and radial and ulnar flexion. The feedback that was used is a custom designed game on the computer (car race) where the car is controlled by four distinct signals recognized from muscle activities recorded with 18 points on the skin (monopolar configuration). The system can be implemented for other games which require four inputs since it operates as the computer peripheral. The system was designed for neurorehabilitation of humans after brain injury or disease but with the intention to be used for personal computer control, dedicated system control, and gaming.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122502394","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: 6
An adequate training set for the AIMNC strategy for typical industrial processes 为典型工业过程的AIMNC战略提供适当的训练集
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011502
Jasmin Igic, M. Bozic
{"title":"An adequate training set for the AIMNC strategy for typical industrial processes","authors":"Jasmin Igic, M. Bozic","doi":"10.1109/NEUREL.2014.7011502","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011502","url":null,"abstract":"Here we have discussed how the training data set should be selected for the Approximate Internal Model-based Neural Control (AIMNC) applied to the typical industrial processes. In the considered control strategy only one neural network (NN), Multi Layer NN (MLNN), which is the neural model of the plant, should be trained off-line. An inverse neural controller can be directly obtained from the neural model without necessity of a further training. Simulations demonstrate performance of the AIMNC strategy for NN model obtained with adequate training set.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587492","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: 1
Feedforward multilayer phase-based neural networks 前馈多层相位神经网络
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011478
I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi
{"title":"Feedforward multilayer phase-based neural networks","authors":"I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi","doi":"10.1109/NEUREL.2014.7011478","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011478","url":null,"abstract":"Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117336286","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: 2
Analysis of spatial partitioning approaches for image classification 图像分类的空间划分方法分析
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011509
A. Avramović, V. Risojevic
{"title":"Analysis of spatial partitioning approaches for image classification","authors":"A. Avramović, V. Risojevic","doi":"10.1109/NEUREL.2014.7011509","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011509","url":null,"abstract":"Spatial partitioning is proven to be beneficial for the tasks of image classification, scene categorization and object recognition. The most popular method to capture rough spatial structure of the scene is spatial pyramid matching. However, spatial pyramid matching results in an image representation that is sensitive to rotations. In this research we investigate the influence of upright and rotated partitions on image classification regardless of the image filtering step. We show that simple combination of rotated spatial partitions improves classification accuracy up to 10% compared to single spatial partition commonly used in spatial pyramid matching.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117315520","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: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信