Catálogo de publicaciones - libros
Advances in Natural Computation: 1st International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
Lipo Wang ; Ke Chen ; Yew Soon Ong (eds.)
En conferencia: 1º International Conference on Natural Computation (ICNC) . Changsha, China . August 27, 2005 - August 29, 2005
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Pattern Recognition; Evolutionary Biology
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-28323-2
ISBN electrónico
978-3-540-31853-8
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2005
Tabla de contenidos
doi: 10.1007/11539087_102
A Novel Local Connection Neural Network
Shuang Cong; Guodong Li; Yisong Zheng
A new type of local connection neural network is proposed in this paper. There is a called K-type activation function in its hidden layer so as to have less computation compared with other local connection neural network. First the structure and algorithm of the proposed network are given. Then the function of network and its properties are analyzed theoretically. The proposed network can be used in the function approximation and modeling. Finally, numerical applications are used to verify the advantages of proposed network compared with other local connection neural networks.
- Other Topics in Neural Network Models | Pp. 773-777
doi: 10.1007/11539087_103
An Unsupervised Cooperative Pattern Recognition Model to Identify Anomalous Massive SNMP Data Sending
Álvaro Herrero; Emilio Corchado; José Manuel Sáiz
In this paper, we review a visual approach and propose it for analysing computer-network activity, which is based on the use of unsupervised connectionist neural network models and does not rely on any previous knowledge of the data being analysed. The presented Intrusion Detection System (IDS) is used as a method to investigate the traffic which travels along the analysed network, detecting SNMP (Simple Network Management Protocol) anomalous traffic patterns. In this paper we have focused our attention on the study of anomalous situations generated by a MIB (Management Information Base) information transfer.
- Other Topics in Neural Network Models | Pp. 778-782
doi: 10.1007/11539087_104
A Fast Nonseparable Wavelet Neural Network for Function Approximation
Jun Zhang; Xieping Gao; Chunhong Cao; Fen Xiao
In this paper, based on the theory of nonseparable wavelet, a novel nonseparable wavelet model has been proposed. The structure of the model is distinguished from that of wavelet network (RBF structure). It is a four-layer structure, which helps overcome the structural redundancy. In the process of the training of the network, in the light of the characteristics of nonseparable wavelet, a novel method of setting the initial value of weight has been proposed. It can overcome the shortcoming of gradient descent methodology that it makes the convergence of the network slow. Some experiments with the novel model for function learning will be shown. Comparing with the present wavelet networks, BP network, the results in this paper show that the speed and generalization performance of the novel model have been greatly improved.
- Other Topics in Neural Network Models | Pp. 783-788
doi: 10.1007/11539087_106
ANN Ensemble Online Learning Strategy in 3D Object Cognition and Recognition Based on Similarity
Rui Nian; Guangrong Ji; Wencang Zhao; Chen Feng
In this paper, in aid of ANN ensemble, a supervised online learning strategy continuously achieves omnidirectional information accumulation for 3D object cognition from 2D view sequence. The notion of similarity is introduced to solve the paradox between information simplicity and accuracy. Images are segmented into homogeneous region for training, correspondent to distinct model views characteristic of neighboring generalization. Real-time techniques are adopted to expand knowledge until satisfactory. The insert into joint model views is only needed in case of impartibility. Simulation experiment has achieved encouraging results, and proved the approach effective and feasible.
- Cognitive Science | Pp. 793-796
doi: 10.1007/11539087_108
Comparison of Complexity and Regularity of ERP Recordings Between Single and Dual Tasks Using Sample Entropy Algorithm
Tao Zhang; Xiaojun Tang; Zhuo Yang
The purpose of this study is to investigate the application of sample entropy (SampEn) measures to electrophysiological studies of single and dual tasking performance. The complexity of short-duration (~s) epochs of EEG data were analysed using SampEn along with the surrogate technique. Individual tasks consisted of an auditory discrimination task and two motor tasks of varying difficulty. Dual task conditions were combinations of one auditory and one motor task. EEG entropies were significantly lower in dual tasks compared to that in the single tasks. The results of this study have demonstrated that entropy measurements can be a useful alternative and nonlinear approach to analyzing short duration EEG signals on a time scale of seconds.
- Cognitive Science | Pp. 806-810
doi: 10.1007/11539087_110
Neural Network Based Emotion Estimation Using Heart Rate Variability and Skin Resistance
Sun K. Yoo; Chung K. Lee; Youn J. Park; Nam H. Kim; Byung C. Lee; Kee S. Jeong
In order to build a human-computer interface that is sensitive to a user’s expressed emotion, we propose a neural network based emotion estimation algorithm using heart rate variability (HRV) and galvanic skin response (GSR). In this study, a video clip method was used to elicit basic emotions from subjects while electrocardiogram (ECG) and GSR signals were measured. These signals reflect the influence of emotion on the autonomic nervous system (ANS). The extracted features that are emotion-specific characteristics from those signals are applied to an artificial neural network in order to recognize emotions from new signal collections. Results show that the proposed method is able to accurately distinguish a user’s emotion.
- Cognitive Science | Pp. 818-824
doi: 10.1007/11539087_112
PENCIL: A Framework for Expressing Free-Hand Sketching in 3D
Zhan Ding; Sanyuan Zhang; Wei Peng; Xiuzi Ye; Huaqiang Hu
This paper presents a framework for expressing free-hand sketching in 3D for conceptual design input. In the framework, sketch outlines will be recognized as formal rigid shapes first. Then under a group of gestures and DFAs’(deterministic finite automata) control, the framework can express user’s free sketching intents freely. Based on this framework, we implemented a sketch-based 3D prototype system supporting conceptual designs. User can easily and rapidly create 3D objects such as hexahedron, sphere, cone, extrusion, swept body, revolved body, lofted body and their assemblies by sketching and gestures.
- Cognitive Science | Pp. 835-838
doi: 10.1007/11539087_114
A Computation Model of Korean Lexical Processing
Hyungwook Yim; Heuseok Lim; Kinam Park; Kichun Nam
This study simulates a lexical decision task in Korean by using a feed forward neural network model with a back propagation learning rule. Reaction time is substituted by a entropy value called ‘semantic stress’. The model demonstrates frequency effect, lexical status effect and non-word legality effect, suggesting that lexical decision is made within a structure of orthographic and semantic features. The test implies that the orthographic and semantic features can be automatically applied to lexical information process.
- Cognitive Science | Pp. 844-849
doi: 10.1007/11539087_116
Cooperative Aspects of Selective Attention
KangWoo Lee
This paper investigates the cooperative aspects of selective attention in which primary (or bottom-up) information is dynamically integrated by the secondary (top-down or context) information from different channels, and in which the secondary information provides a criterion of what should be many target candidates We present a computational model of selective attention that implements these cooperative behaviors. Simulation results, obtained using still and video images, are presented showing the interesting properties of the model that are not captured by only competitive aspects of selective attention.
- Cognitive Science | Pp. 855-866
doi: 10.1007/11539087_118
Visual Search for Object Features
Predrag Neskovic; Leon N Cooper
In this work we present the computational algorithm that combines perceptual and cognitive information during the visual search for object features. The algorithm is initially driven purely by the bottom-up information but during the recognition process it becomes more constrained by the top-down information. Furthermore, we propose a concrete model for integrating information from successive saccades and demonstrate the necessity of using two coordinate systems for measuring feature locations. During the search process, across saccades, the network uses an object-based coordinate system, while during a fixation the network uses the retinal coordinate system that is tied to the location of the fixation point. The only information that the network stores during saccadic exploration is the identity of the features on which it has fixated and their locations with respect to the object-centered system.
- Cognitive Science | Pp. 877-887