Dynamic feature-based expansion of fuzzy sets in Neuro-Fuzzy for proactive malware detection

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

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Web-Enabled Feature-Based Modeling in a Distributed Design Environment

Volume 4: 4th Design for Manufacturing Conference ◽

10.1115/detc99/dfm-8941 ◽

1999 ◽

Author(s):

Jae Yeol Lee ◽

Hyun Kim ◽

Sung-Bae Han

Keyword(s):

Collaborative Design ◽

Feature Modeling ◽

Feature Model ◽

Internet Technology ◽

Global Network ◽

Dynamic Feature ◽

Distributed Design ◽

Product Modeling ◽

Design Environment ◽

Feature Based

Abstract Network and Internet technology open up another domain for building future CAD/CAM environments. The environment will be global, network-centric, and spatially distributed. In this paper, we present Web-enabled feature-based modeling in a distributed design environment. The presented approach combines the current feature-based modeling technique with distributed computing and communication technology for supporting product modeling and collaborative design activities over the network. The approach is implemented in a client/server architecture, in which Web-enabled feature modeling clients, neutral feature model server, and other applications communicate with one another via a standard communication protocol. The paper discusses how the neutral feature model supports multiple views and maintains naming consistency between geometric entities of the server and clients as the user edits the part in a client. Moreover, it explains how to minimize the network delay between the server and client according to dynamic feature modeling operations.

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A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Mathematical Problems in Engineering ◽

10.1155/2016/6848360 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Tsun-Kuo Lin

Keyword(s):

Wavelet Transform ◽

Region Growing ◽

Support Vector ◽

Manufacturing Processes ◽

Dynamic Feature ◽

Classification Methods ◽

Multiple Objects ◽

Multiple Object ◽

Feature Based ◽

Inspection Schemes

Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

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