IEEE International Joint Conference on Neural Network最新文献_第9页

OntoHop: An information filtering agent using hopfield nets and ontologies OntoHop:使用hopfield网络和本体的信息过滤代理

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2012.6252796

J. Coello, Carlos Miguel Tobar Toledo

{"title":"OntoHop: An information filtering agent using hopfield nets and ontologies","authors":"J. Coello, Carlos Miguel Tobar Toledo","doi":"10.1109/IJCNN.2012.6252796","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252796","url":null,"abstract":"The size of the Web and its dynamic nature in addition to the fact that stored documents are written in natural language, and therefore intended to be read by people and not to be processed by computers, present major challenges to build automatic personalized information filtering systems. This article presents the architecture of an information filtering agent based on an implementation of a Hopfield neural network (HNN). Network nodes (neurons) represent relevant terms in the domain of interest and neuronal links represent asymmetric probabilities of term co-occurrences in the domain, or the relevance weight between a pair of terms. Relevant terms are automatically derived from a corpus related to the domain of interest using automatic indexing and an ontology. Co-occurrence probabilities are computed by a cluster function that produces asymmetric links between terms. At the moment of document filtering, input neurons are activated on the basis of the presence of terms in the document that are identical or semantically similar to the terms stored in the net. The semantic similarity between terms is calculated using a hierarchical ontology that describes concepts that exist in the domain of interest. Experiments conducted to evaluate the precision and recall of the agent with and without the use of ontologies show that ontology use tends to favor recall over precision. The degree to which this bias occurs can be adjusted by setting the minimum level of similarity required to consider a document and a network term similar.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807061","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 simple synchronization criterion for a complex network based on the feedback 基于反馈的复杂网络的简单同步准则

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2012.6252702

Mo Zhao, Huaguang Zhang, Zhiliang Wang, Yanhong Luo

引用次数: 0

A model of cooperative agent based on imitation and Maslow's Pyramid of needs 基于模仿和马斯洛需求金字塔的合作主体模型

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2009.5178916

H. L. Guen, S. Moga

引用次数: 6

Dimensionality reduction by self organizing maps that preserve distances in output space 通过在输出空间中保持距离的自组织映射来降低维数

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2009.5179009

P. Cervera

{"title":"Dimensionality reduction by self organizing maps that preserve distances in output space","authors":"P. Cervera","doi":"10.1109/IJCNN.2009.5179009","DOIUrl":"https://doi.org/10.1109/IJCNN.2009.5179009","url":null,"abstract":"Dimensionality Reduction is a key issue in many scientific problems, in which data is originally given by high dimensional vectors, all of which lie however over a fewer dimensional manifold. Therefore, they can be represented by a reduced number of values that parametrize their position over the mentioned non-linear manifold. This dimensionality reduction is essential not only for representing and managing data, but also for its understanding at a high interpretation level, similar to the way it is performed by the mammal cortex. This paper presents an algorithm for representing the data that lie on a non-linear manifold by the reduced number of their coordinates along a grid or map of neurons extended over this manifold. This map is generated by a Self-organization learning process whose key feature is the fact that the winning neuron is selected in order to preserve distances of input data when they are represented by their coordinates in the output map. Unlike other methods, the proposed algorithm has important features, that namely the intrinsic dimensionality is obtained simultaneously in the learning process itself, it doesn't require a long course positioning phase, and it seeks to maintain the data structure from the beginning, not leaving it as an ulterior fact to be proven. The algorithm has proven to efficiently solve classical dimensionality reduction problems, and has also showed that it can be useful for realistic problems, such as face images classification or document indexing.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125147209","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: 16

Multi-GPU based camera network system keeps privacy using growing neural gas 基于多gpu的摄像机网络系统利用不断增长的神经气体保护隐私

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2012.6252805

Sergio Orts, J. G. Rodríguez, Vicente Morell, J. A. López, J. Chamizo

{"title":"Multi-GPU based camera network system keeps privacy using growing neural gas","authors":"Sergio Orts, J. G. Rodríguez, Vicente Morell, J. A. López, J. Chamizo","doi":"10.1109/IJCNN.2012.6252805","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252805","url":null,"abstract":"In this work we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events in video. The objectives include: identifying and tracking persons or objects in the scene or the interpretation of user gestures for interaction with services, devices and systems implemented in the digital home. Additionally, the system process several tasks in parallel using GPUs (Graphic Processor Units). Addressing multiple vision tasks of various levels such as segmentation, representation or characterization, analysis and monitoring of the movement to allow the construction of a robust representation of their environment and interpret the elements of the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency and offering relevant information to higher level systems, monitor and take decisions in real time, and must accomplish a set of requirements such as: time constraints, high availability, robustness, high processing speed and re-configurability. Based on our previous work with Growing Neural Gas (GNG) models, we have built a system able to represent and analyze the motion in several image sequences acquired by a multi-camera network and process multisource data in parallel onto a Multi-GPU architecture. The system is able to keep the privacy of the persons under observation by using the graph representation provided by the GNG. Several experiments are presented that demonstrate the validity of the architecture to manage images from different cameras simultaneously.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116694532","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

Deep Spiking Neural Network for High-Accuracy and Energy-Efficient Face Action Unit Recognition 高精度节能人脸动作单元识别的深度脉冲神经网络

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN52387.2021.9533451

Jingren Zhang, Jingjing Wang, Jingwei Yan, Chunmao Wang, Shiliang Pu

引用次数: 2

Neural Networks Applied in the Prediction of Top Oil Temperature of Transformer 神经网络在变压器最高油温预测中的应用

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2019.8852072

W. Pan, Kun Zhao, T. Gao, Congchuang Gao

{"title":"Neural Networks Applied in the Prediction of Top Oil Temperature of Transformer","authors":"W. Pan, Kun Zhao, T. Gao, Congchuang Gao","doi":"10.1109/IJCNN.2019.8852072","DOIUrl":"https://doi.org/10.1109/IJCNN.2019.8852072","url":null,"abstract":"Top oil temperature (TOT) is an important indicator reflecting the load capacity and insulation aging of the transformer. In order to predict the TOT accurately, this paper propose a transformer top oil temperature prediction method based on BP neural networks optimized by Adam. Firstly, we use the grey relational analysis method to calculate the correlation between other state variables of the transformer and the TOT, select state variables with larger correlation as the inputs and the TOT as the output to establish the neural networks prediction model (NNPM) of the TOT. Next NNPM of TOT is trained using historical data of transformer and Adam optimization algorithm. Then the case studying for historical data suggests that the prediction results of NNPM optimized by Adam of TOT are in accordance with measured results. Comparing with D Susa thermal circuit model and NNPM trained by SGD, the prediction accuracy of NNPM optimized by Adam is improved by 78.1% and 33.95% respectively. Finally, we choose different transformers to model and predict, and the results show that NNPM of TOT based on Adam has applicable ability to different transformers. The top oil temperature prediction method proposed in this paper provides a more accurate calculation basis for prediction of TOT of transformers and is of great significance for the safe and stable operation of the power transformers.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114206794","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

Structural information processing in early vision using Hebbian-based mean shift 基于hebbian均值移位的早期视觉结构信息处理

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2013.6707138

Jiqian Liu, Caixia Zhang, Yunde Jia

{"title":"Structural information processing in early vision using Hebbian-based mean shift","authors":"Jiqian Liu, Caixia Zhang, Yunde Jia","doi":"10.1109/IJCNN.2013.6707138","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6707138","url":null,"abstract":"This paper presents a biologically plausible model for the structural information processing in early vision. Our investigation on the frequency spectrum of natural images filtered by the retina shows that the DC component containing much redundancy information and the high frequency components containing much noisy information are reduced, while the middle and low frequency components containing much structural information are enhanced. Simple cells in the primary visual cortex (V1) extract structural primitives from the filtered signals resulting in the emergence of diverse receptive field shapes. We name these structural primitives as structors, and study the neural mechanisms responsible for this diversity of V1 simple cell receptive field shapes. Sparse coding with the L0-norm constraint is reexamined which suggests that the local structure of natural images is determined by few structors regardless of their coefficients. We perform an analysis on the spatial distribution of the input signal and prove that signals in the neighborhood of a special structor has a star shape and peaks at the structor. That is, the structors are the modes of the probability density function of the input signal, and learning the structors can be interpreted as mode detection. Mean sift method is applied to detect modes, and the updating rule for the mean shift appears to be Hebbian. We propose the Hebbian-based mean shift to simulate the emergence of the diversity of simple cell receptive field shapes. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like structors.","PeriodicalId":134599,"journal":{"name":"IEEE International Joint Conference on Neural Network","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115453854","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

Metric Learning for Electrical Submersible Pump Fault Diagnosis 电潜泵故障诊断的度量学习

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN48605.2020.9207133

L. H. S. Mello, M. Ribeiro, Thiago Oliveira-Santos, F. M. Varejão, Alexandre Rodrigues Loureiros

引用次数: 5

Using Multi-objective Algorithms for Optimizing Support Vector Regression Parameters 基于多目标算法的支持向量回归参数优化

IEEE International Joint Conference on Neural Network Pub Date : 1900-01-01 DOI: 10.1109/IJCNN.2018.8489334

M. Neto, Roberta Fagundes, C. J. A. B. Filho

引用次数: 1