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2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)最新文献

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Non-rigid 3D shape classification based on convolutional neural networks 基于卷积神经网络的非刚性三维形状分类
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285693
Jan Franco Llerena Quenaya, C. L. D. Alamo
{"title":"Non-rigid 3D shape classification based on convolutional neural networks","authors":"Jan Franco Llerena Quenaya, C. L. D. Alamo","doi":"10.1109/LA-CCI.2017.8285693","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285693","url":null,"abstract":"Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a “spectral image”. By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and also it is robust under several types of transformations.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117197885","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
A clustering-based deep autoencoder for one-class image classification 一种基于聚类的图像分类深度自编码器
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285680
M. Gutoski, Manassés Ribeiro, Nelson Marcelo Romero Aquino, A. Lazzaretti, H. S. Lopes
{"title":"A clustering-based deep autoencoder for one-class image classification","authors":"M. Gutoski, Manassés Ribeiro, Nelson Marcelo Romero Aquino, A. Lazzaretti, H. S. Lopes","doi":"10.1109/LA-CCI.2017.8285680","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285680","url":null,"abstract":"Anomaly detection in images is a topic of great interest in Computer Vision. It can be defined as an One-Class problem, where the goal is to detect deviations from known patterns, which are defined as normal. Recently, Deep Learning methods became popular due to their performance on classification tasks. This works presents an image anomaly detection classifier based on a previously known method, the Deep Embedded Clustering, which is based on a Deep Autoencoder. We show the effectiveness of the method through three different experiments. Results suggest that the method improves classification performance when compared to a Stacked Denoising Autoencoder in the image anomaly detection context.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134355684","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: 21
Real-time neural backstepping control for a helicopter prototype 直升机原型机的实时神经反步控制
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285689
Larbi Djilali, Oscar J. Suarez, E. Sánchez, A. Alanis, Aldo Pardo García
{"title":"Real-time neural backstepping control for a helicopter prototype","authors":"Larbi Djilali, Oscar J. Suarez, E. Sánchez, A. Alanis, Aldo Pardo García","doi":"10.1109/LA-CCI.2017.8285689","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285689","url":null,"abstract":"This paper presents a discrete-time backstepping controller based on a neural model for a Quanser 2-Degree Of Freedom (DOF) helicopter. The proposed controller is used to track the pitch and yaw position references independently. This controller is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF). The RHONN works as an identifier to obtain an adequate Quanser 2-DOF helicopter mathematic model, which is robust in presence of disturbances and parameter variations. To examine the robustness of the proposed controller, simulations using Matlab/Simulinkand real-time implementation are presented.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126743357","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
Real-time neural optimal controller for a direct expansion (DX) air conditioning (A/C) system 直接膨胀式空调(DX)系统的实时神经优化控制器
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285686
Flavio Muñoz, E. Sánchez, Yudong Xia, S. Deng
{"title":"Real-time neural optimal controller for a direct expansion (DX) air conditioning (A/C) system","authors":"Flavio Muñoz, E. Sánchez, Yudong Xia, S. Deng","doi":"10.1109/LA-CCI.2017.8285686","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285686","url":null,"abstract":"A discrete-time neural inverse optimal control scheme for the simultaneous control of indoor air temperature and humidity of a DX A/C system is reported in this paper. The plant model is identified using a recurrent high order neural network (RHONN), and a discrete-time inverse optimal control law is derived with this model. Kalman filtering is used to perform on-line the neural network learning. This novel proposed control scheme is tested via implementation in real time. The obtained results for trajectory tracking illustrate the effectiveness of the proposed approach.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123635559","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
Clustering of regional HDI data using Self-Organizing Maps 基于自组织地图的区域HDI数据聚类
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285699
J. A. F. Costa, A. Pinto, Joao Ribeiro de Andrade, Marcial Guerra de Medeiros
{"title":"Clustering of regional HDI data using Self-Organizing Maps","authors":"J. A. F. Costa, A. Pinto, Joao Ribeiro de Andrade, Marcial Guerra de Medeiros","doi":"10.1109/LA-CCI.2017.8285699","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285699","url":null,"abstract":"The Municipal Human Development Index (HDI) was established by the United Nations Development Program. It uses data from longevity, education, and income to infer regional social and economic life quality. This work developed a multivariate analysis of the HDI data evolution in years 1991, 2000 and 2010 of 167 municipalities in Rio Grande do Norte State, northeast Brazil. Self-organizing (Kohonen or SOM) maps were used to perform clustering and data visualization. The approach uses map segmentation with k-means algorithm after SOM training. Transition analysis from municipalities in different studied years are performed, presenting ranking of clusters in terms of the three main HDI dimensions. Five groups of municipalities resulted from intragroup similarities and intergroup dissimilarities in each period. The segmented maps present similar municipalities. Thematic maps of the region after data clustering are also shown.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134296441","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
User profile acquisition: A comprehensive framework to support personal information agents 用户配置文件获取:支持个人信息代理的综合框架
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285719
G. Kasper, D. D. S. Braga, D. Martins, B. Hellingrath
{"title":"User profile acquisition: A comprehensive framework to support personal information agents","authors":"G. Kasper, D. D. S. Braga, D. Martins, B. Hellingrath","doi":"10.1109/LA-CCI.2017.8285719","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285719","url":null,"abstract":"Personal information agents play an important role in the acquisition, processing, and distribution of information and are of utmost importance in many areas. However, in order to meet user's information needs, these agents must direct their tasks by reasoning upon the user-related information. In this sense, user profiling plays a crucial part. User profiling is an attempt to deal with the information about Internet users and its diversity and complexity through categorization. In this paper, an overview of the user profile acquisition is provided. For this purpose, a framework is presented which describes the most important aspects and frame conditions of user profile acquisition. The three user profile types Explicit, Implicit, and Hybrid are discussed, and user profile acquisition and enrichment techniques are elaborated for each of them. Furthermore, the challenges regarding user profile acquisition and the legal requirements are presented.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128707156","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
Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification 基于潜在语义索引和卷积神经网络的多标签多类文本分类
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285711
Oscar Quispe, Alexander Ocsa, R. Coronado
{"title":"Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification","authors":"Oscar Quispe, Alexander Ocsa, R. Coronado","doi":"10.1109/LA-CCI.2017.8285711","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285711","url":null,"abstract":"The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133610436","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: 10
Deep neural network for EMG signal classification of wrist position: Preliminary results 腕部位置肌电图信号分类的深度神经网络:初步结果
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285706
A. Orjuela-Cañón, A. F. R. Olaya, Leonardo Forero
{"title":"Deep neural network for EMG signal classification of wrist position: Preliminary results","authors":"A. Orjuela-Cañón, A. F. R. Olaya, Leonardo Forero","doi":"10.1109/LA-CCI.2017.8285706","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285706","url":null,"abstract":"Physically impaired people may use Surface Electromyography (SEMG) signals to control rehabilitation and assistive devices. SEMG is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. SEMG directly reflects the human motion intention; thus, they can be used as input information for human-robot interaction. This paper proposes an EMG-based pattern recognition algorithm for classification of joint wrist angular position during flexion-extension movements from EMG signals. The algorithm uses a feature extraction stage based on a combination of time and frequency domain. The pattern recognition stage uses an artificial neural network (NN) as classifier. Also, using an autoencoder, deep NN architecture was tested. It was carried out a set of experiment with 10 subjects. Experiments included five recorded SEMG channels from forearm executing wrist flexion and extension movements, as well as the use of a commercial electrogoniometer to acquire joint angle. Results show that shallow NN had better performance that architectures with more layers based on autoencoders.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084100","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: 18
Clustering algorithms applied on analysis of protein molecular dynamics 聚类算法在蛋白质分子动力学分析中的应用
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285695
Vinicius Carius de Souza, L. Goliatt, P. V. Z. C. Goliatt
{"title":"Clustering algorithms applied on analysis of protein molecular dynamics","authors":"Vinicius Carius de Souza, L. Goliatt, P. V. Z. C. Goliatt","doi":"10.1109/LA-CCI.2017.8285695","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285695","url":null,"abstract":"Analysis of molecular dynamic (MD) simulation has been difficult since this method generates a lot of conformations. Thus clustering algorithms have been applied to group similar structures from MD simulations, but the choice of the information to be clustered is still a challenge. In this work, we propose the use of Euclidean distance matrices (EDM) from conformations as input data to clustering algorithms. We used approaches combining non-reduction or reduction of data dimensionality (MDS and isomap methods), and different clustering algorithms (k-means, ward, mean-shift and affinity propagation). Results indicated that EDM could be a good information to be used in clustering conformations from MD. For data with small protein structure variation, the mean-shift algorithm had good results in both non-reduced and reduced data. However, for data with large protein structure variation, the methods that work better with smooth-density data (k-means and ward) had good results.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122575244","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: 8
Pedestrian detection in digital videos using committee of motion feature extractors 基于运动特征提取器的数字视频行人检测
2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Pub Date : 2017-11-01 DOI: 10.1109/LA-CCI.2017.8285714
Diogo L. da Silva, L. Seijas, C. J. A. B. Filho
{"title":"Pedestrian detection in digital videos using committee of motion feature extractors","authors":"Diogo L. da Silva, L. Seijas, C. J. A. B. Filho","doi":"10.1109/LA-CCI.2017.8285714","DOIUrl":"https://doi.org/10.1109/LA-CCI.2017.8285714","url":null,"abstract":"Human detection in digital images is a challenge because the motion of the camera, background and variations in pose, appearance, clothing and illumination introduce difficulties for person detection. Several pedestrian detectors were proposed recently, such as the Aggregated Channel Features (ACF). These types of detectors are based on features related to the shape of the object. These detectors generate many false alarms. In this paper we propose the use of motion features in the ACF framework to mitigate false alarms emitted. Three motion features are proposed: WSTD, MBH and IMHcd. We combined these features with ACF. Then, committees of classifiers were created from these feature combinations improving original ACF results and reducing false positives per image (FPPI). An improvement on the miss rate for 100 FPPI and on the log-average miss rate was obtained, reducing these values in 19 and 8.46 percentage points respectively on Caltech dataset.","PeriodicalId":144567,"journal":{"name":"2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127148358","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
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