IEEE International Joint Conference on Neural Network最新文献

Improved Stochastic Synapse Reinforcement Learning for Continuous Actions in Sharply Changing Environments 在急剧变化的环境中连续动作的改进随机突触强化学习

IEEE International Joint Conference on Neural Network Pub Date : 2020-07-01 DOI: 10.1109/IJCNN48605.2020.9207622

Syed Naveed Hussain Shah, Dean Frederick Hougen

引用次数: 0

Unsupervised Embeddings for Categorical Variables 分类变量的无监督嵌入

IEEE International Joint Conference on Neural Network Pub Date : 2020-07-01 DOI: 10.1109/IJCNN48605.2020.9207703

Hannes De Meulemeester, B. Moor

引用次数: 1

A Continuous Restricted Boltzmann Machine and Logistic Regression Framework for Circuit Classification 电路分类的连续受限玻尔兹曼机与逻辑回归框架

IEEE International Joint Conference on Neural Network Pub Date : 2020-07-01 DOI: 10.1109/IJCNN48605.2020.9207323

L. M. Silva, F. V. Andrade, L. Vieira

引用次数: 2

An Innovative Approach of Textile Fabrics Identification from Mobile Images using Computer Vision based on Deep Transfer Learning 基于深度迁移学习的计算机视觉移动图像织物识别创新方法

IEEE International Joint Conference on Neural Network Pub Date : 2020-07-01 DOI: 10.1109/IJCNN48605.2020.9206901

A. C. D. S. Barros, E. F. Ohata, S. P. P. Silva, Jefferson Silva Almeida, P. P. R. Filho

{"title":"An Innovative Approach of Textile Fabrics Identification from Mobile Images using Computer Vision based on Deep Transfer Learning","authors":"A. C. D. S. Barros, E. F. Ohata, S. P. P. Silva, Jefferson Silva Almeida, P. P. R. Filho","doi":"10.1109/IJCNN48605.2020.9206901","DOIUrl":"https://doi.org/10.1109/IJCNN48605.2020.9206901","url":null,"abstract":"The identification of different textile fabrics is a task commonly learned in practice and, therefore, is considered a very strenuous and costly form of learning, causing annoyance to the individual who performs it. Based on this context, this paper proposes a new method for classifying textile fabrics, based on the development of a computer vision system using Convolutional Neural Network (CNN). CNN works as a feature extractor by incorporating the concept of Transfer Learning. Using Transfer Learning allows a pre-trained CNN model to be reused for a new problem. In order to highlight the high performance of CNN, an analysis is performed with feature extractors established in the literature. Parameters such as Accuracy, F1-Score, and processing time are considered to evaluate the efficiency of the proposed approach. For the classification were used Bayesian Classifier, Multi-layer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM). The results show that the best combination is the CNN architecture DenseNet201 with SVM (RBF), obtaining an accuracy of 94% and F1-Score of 94.2%.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228122","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

Predicting Human Errors from Gaze and Cursor Movements 从凝视和光标移动预测人类错误

IEEE International Joint Conference on Neural Network Pub Date : 2020-07-01 DOI: 10.1109/IJCNN48605.2020.9207189

R. R. Saboundji, R. Rill

{"title":"Predicting Human Errors from Gaze and Cursor Movements","authors":"R. R. Saboundji, R. Rill","doi":"10.1109/IJCNN48605.2020.9207189","DOIUrl":"https://doi.org/10.1109/IJCNN48605.2020.9207189","url":null,"abstract":"Intelligent interfaces are increasingly integrated into diverse technological areas. In complex high-risk environments, where humans represent a crucial part of the system and their attention is often divided between simultaneous activities, imminent human errors may have serious consequences. Enhancing interfaces with predictive capabilities promotes the safe and reliable operation of such systems. In this work, we employ a data-driven approach to predict human errors in a special divided attention task involving timing constraints and requiring focused concentration and frequent shifts of attention. We performed a longitudinal study with 10 subjects, and constructed time series from the experimental data using gaze movement and mouse cursor motion features in order to classify successful and failed actions. We evaluate classical machine learning algorithms, compare them with a more traditional temporal modeling approach and a deep learning based LSTM model. Employing a leave-one- subject-out cross-validation procedure we achieve a classification accuracy of up to 86%, with LSTM presenting the highest performance. Furthermore, we investigate the trade-off between evaluation metrics and anticipation window, i.e. the time remaining until the correct action can still be performed. We conclude that prediction is feasible and accuracy and F1-score increases, despite the training dataset becoming greatly imbalanced. Investigating the anticipation window allows to understand how far in advance human errors need to be predicted in order to initiate preventive measures. Our efforts have implications for the design of predictive interfaces involving decision making under time pressure in dynamic divided attention environments.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121995860","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

A Method for Voltage Sag Source Location Using Clustering Algorithm and Decision Rule Labeling 基于聚类算法和决策规则标记的电压暂降源定位方法

IEEE International Joint Conference on Neural Network Pub Date : 2019-07-01 DOI: 10.1109/IJCNN.2019.8852170

J. L. E. S. Filho, F. A. S. Borges, R. Rabêlo, Ivan Saraiva Silva

{"title":"A Method for Voltage Sag Source Location Using Clustering Algorithm and Decision Rule Labeling","authors":"J. L. E. S. Filho, F. A. S. Borges, R. Rabêlo, Ivan Saraiva Silva","doi":"10.1109/IJCNN.2019.8852170","DOIUrl":"https://doi.org/10.1109/IJCNN.2019.8852170","url":null,"abstract":"The voltage sag disturbance stands out as the most evident waveform change that is detected in electric networks, since the presence of these events in the network causes damages to the consumers. The first step in diagnosing the problem is to identify the location in the distribution system that is connected to the source causing the sinking disorder. This work presents a methodology based on clustering algorithm combined with decision rule to point out the region (cluster) that aggregates the place of origin. Clustering algorithm is responsible for analyzing the voltage signal data from different measurement nodes and separating these data into clusters. Then the Partial Decision Trees (PART) algorithm is responsible for defining the decision rule set that will confront the characteristics of each cluster and define which group aggregates the disturbance source location. For the clustering task, the k-means and fuzzy c-means clustering algorithms are evaluated and compared. The methodology was evaluated using the IEEE 34-bus test feeder system and the results show a hit rate higher than 90%.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133851936","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

Classification of Power Quality Disturbances Using Convolutional Network and Long Short-Term Memory Network 基于卷积网络和长短期记忆网络的电能质量扰动分类

IEEE International Joint Conference on Neural Network Pub Date : 2019-07-01 DOI: 10.1109/IJCNN.2019.8852287

W. L. R. Junior, F. A. S. Borges, R. Rabêlo, Bruno Vicente Alves de Lima, Jose Eduardo Almeida de Alencar

{"title":"Classification of Power Quality Disturbances Using Convolutional Network and Long Short-Term Memory Network","authors":"W. L. R. Junior, F. A. S. Borges, R. Rabêlo, Bruno Vicente Alves de Lima, Jose Eduardo Almeida de Alencar","doi":"10.1109/IJCNN.2019.8852287","DOIUrl":"https://doi.org/10.1109/IJCNN.2019.8852287","url":null,"abstract":"The Electrical Power Quality (PQ) studies are commonly related to disturbances that alter the sinusoidal voltage features and/or current wave shapes. The classification approaches of electrical power quality disturbances found in the literature mainly consist of three steps: 1) signal analysis and feature extraction, 2) feature selection and 3) disturbances classification. However, there are some problems inherent in disturbances classification. The manual extraction of features is an imprecise and complex process, which can influence the resuits and, therefore, does not deal well with noisy signals. This paper proposes an approach based on Deep Learning using the raw data, without pre-processing, manual extraction or manual feature selection of the PQ disturbances signals for the classification of fifteen electrical power quality disturbances. A deep network is used, which consists of a hybrid architecture, composed by convolutional layers, a pooling layer, an LSTM layer, and batch normalization to extract features automatically. We adopted a 1-D convolution to adapt the input. The extracted features are used as input to fully connected layers, the last one being a SoftMax layer. The results are compared with state of the art methods based on the three steps, showing that the proposed approach had satisfactory performance even with noisy data.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124541108","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: 17

A Discretization-based Ensemble Learning Method for Classification in High-Speed Data Streams 高速数据流中基于离散化的集成学习分类方法

IEEE International Joint Conference on Neural Network Pub Date : 2019-07-01 DOI: 10.1109/IJCNN.2019.8851703

João Roberto Bertini Junior

{"title":"A Discretization-based Ensemble Learning Method for Classification in High-Speed Data Streams","authors":"João Roberto Bertini Junior","doi":"10.1109/IJCNN.2019.8851703","DOIUrl":"https://doi.org/10.1109/IJCNN.2019.8851703","url":null,"abstract":"Data stream mining has attracted much attention of the machine learning community in the last decade. Motivated by the upcoming issues associated with data stream applications, such as concept drift and the velocity into which data needs to be processed, several methods have been proposed in the literature, most of them resulting from adaptations of traditional algorithms. Such methods are forced to satisfy hard requirements of restricted memory and processing time, while keeping track of the performance at the same time. In the classification context, ensembles are an effective and elegant way to handle this task. And mostly, the bottleneck of processing time and memory of an ensemble relies on the employed base learner and on the ensemble updating policy. This paper addresses both issues by proposing: 1) a fast base learning algorithm, which relies on discretizing every attribute range into disjoint intervals and associating, to each of them, a posterior probability relating it to a class; and 2) a static ensemble that comprises such base learners and handles concept drift without replacing base learners. Results comparing the proposed ensemble method to six ensemble approaches, on artificial and real data streams, showed it yields comparable results but with lower computational time; which makes the proposed ensemble an efficient alternative to high-speed data streams.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117330848","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

Eye Detection Using Ensemble of Weak Classifiers Based on Correlation Filter 基于相关滤波的弱分类器集成眼部检测

IEEE International Joint Conference on Neural Network Pub Date : 2018-07-01 DOI: 10.1109/IJCNN.2018.8489195

Wesley L. Passos, G. Araujo, A. Lima, F. Ribeiro, E. Silva

{"title":"Eye Detection Using Ensemble of Weak Classifiers Based on Correlation Filter","authors":"Wesley L. Passos, G. Araujo, A. Lima, F. Ribeiro, E. Silva","doi":"10.1109/IJCNN.2018.8489195","DOIUrl":"https://doi.org/10.1109/IJCNN.2018.8489195","url":null,"abstract":"This work proposes a novel system for detecting racial landmarks in images using an ensemble of correlation-based filters known as Inner Product Detector (IPD). This work has three main contributions: i) the usage of a bootstrap aggregating algorithm (bagging), to produce a ensemble classifier with higher accuracy when compared with the original IPD detector; ii) a new discriminant function based on the highest IPD mean value calculated from samples positively classified in a voting scheme; iii) and a study to assess the influence of class unbalance over the system performance. The proposed method was evaluated on the BioID and LFPW datasets, achieving an average accuracy of 93.3% in the BioID for both eyes, at 10% of the interocular distance, and accuracies of 85.2% and 81.6% for the left eye and right eyes respectively, on the LFPW database, at 10% of the interocular distance. Since it can detect the eyes at approximately 70 FPS in a Matlab implementation, the proposed method is also fast enough to be used in real time applications. These results were compared to the ones in the state of the art in eye detection - which include methods using deep learning - in terms of accuracy and computational complexity.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128731450","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

Exploring Data Augmentation to Improve Music Genre Classification with ConvNets 利用卷积神经网络探索数据增强以改进音乐类型分类

IEEE International Joint Conference on Neural Network Pub Date : 2018-07-01 DOI: 10.1109/IJCNN.2018.8489166

R. L. Aguiar, Yandre M. G. Costa, C. Silla

{"title":"Exploring Data Augmentation to Improve Music Genre Classification with ConvNets","authors":"R. L. Aguiar, Yandre M. G. Costa, C. Silla","doi":"10.1109/IJCNN.2018.8489166","DOIUrl":"https://doi.org/10.1109/IJCNN.2018.8489166","url":null,"abstract":"In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121818285","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: 12