10134892
Parameterized Freeform Shape Design and Deformation
Deformation is the most widely used in product design. Interactive free-form deformation (FFD), however, is often difficult to generate a desired result by designers. And it can not explicitly specify the degree of deformations such as angle of bend. Therefore, in order to solve these problems, a parametric deformation method driven by nature product definition parameters is presented in the paper. It consists of two phases. Firstly, based on mesh segmentation using feature point and core extraction, the proposed method extracts some product definition parameters automatically. Secondly, regarding these parameters as hard constraints, the deformation process is stably and precisely performed by the constraint optimization. Comparing to previous FFD methods, the proposed method has several distinctive benefits: designers can get the expected deformation efficiently, and then form the product family easily. Finally, shoe industry application of the method in product design is demonstrated. The dimension and style of shoe-last can be changed rapidly to achieve new product design by editing parameters.
5724720
Heterogeneous CAD Hybrid Data Exchange Based on Feature Semantics and Geometry Representation
Product data exchange (DE) is a key technology to implement collaborative design of heterogeneous CAD. Based on analyzing layered expression of product CAD model and current methods of DE, heterogeneous CAD hybrid data exchange (HDE) based on feature semantics and geometry representation is presented. The method integrates advantages of feature semantics exchange (FSE) and geometric data exchange (GDE). It features high flexibility and reliability. A case illustrates the validity of the method
30413553
Reverse innovative design - an integrated product design methodology
Today's product designer is being asked to develop high quality, innovative products at an ever increasing pace. To meet this need, an intensive search is underway for advanced design methodologies that facilitate the acquisition of design knowledge and creative ideas for later reuse. Additionally, designers are embracing a wide range of 3D digital design applications, such as 3D digitization, 3D CAD and CAID, reverse engineering (RE), CAE analysis and rapid prototyping (RP). In this paper, we propose a reverse engineering innovative design methodology called Reverse Innovative Design (RID). The RID methodology facilitates design and knowledge reuse by leveraging 3D digital design applications. The core of our RID methodology is the definition and construction of feature-based parametric solid models from scanned data. The solid model is constructed with feature data to allow for design modification and iteration. Such a construction is well suited for downstream analysis and rapid prototyping. In this paper, we will review the commercial availability and technological developments of some relevant 3D digital design applications. We will then introduce three RE modelling strategies: an autosurfacing strategy for organic shapes; a solid modelling strategy with feature recognition and surface fitting for analytical models; and a curve-based modelling strategy for accurate reverse modelling. Freeform shapes are appearing with more frequency in product development. Since their ''natural'' parameters are hard to define and extract, we propose construction of a feature skeleton based upon industrial or regional standards or by user interaction. Global and local product definition parameters are then linked to the feature skeleton. Design modification is performed by solving a constrained optimization problem. A RID platform has been developed and the main RE strategies and core algorithms have been integrated into SolidWorks as an add-in product called ScanTo3D. We will use this system to demonstrate our RID methodology on a collection of innovative consumer product design examples.
16274066
Density-based multipath secure communication over mobile ad hoc networks
Security is a critical issue in mobile ad hoc networks (MANETs), where mobile nodes communicate with each other over relatively unreliable wireless links with no fixed infrastructure. One example of this occurs on battle fields, where soldiers often wear cameras and wireless mobile devices to send or receive images/video of different angles or locations in order to obtain better observation of the area. Such an environment could be highly insecure since an enemy could place devices to eavesdrop on or compromise the soldier nodes. In this paper, we consider a case where time sensitive multimedia is sent using multiple paths which can make better use of load balancing strategies, increase bandwidth and save node energy. However, an enemy node could listen in on more than one path at the same time, allowing it to collect sufficient shares of an image or video frame to recover the image. Our proposed algorithm is based on a quantitative analysis of security to protect against such multi-path eavesdropping. We prove that our algorithm can achieve better level of security by detouring traffic via paths without or with less vulnerable areas at the same amount of redundancy. On the other hand, we also prove, using simulation results and mathematical derivation, our algorithm can provide much more redundancy for better multimedia quality purpose while at the same time guaranteeing the same level of security.
17433940
Construction of Quasi-Cyclic LDPC Codes for AWGN and Binary Erasure Channels: A Finite Field Approach
In the late 1950s and early 1960s, finite fields were successfully used to construct linear block codes, especially cyclic codes, with large minimum distances for hard-decision algebraic decoding, such as Bose-Chaudhuri-Hocquenghem (BCH) and Reed-Solomon (RS) codes. This paper shows that finite fields can also be successfully used to construct algebraic low-density parity-check (LDPC) codes for iterative soft-decision decoding. Methods of construction are presented. LDPC codes constructed by these methods are quasi-cyclic (QC) and they perform very well over the additive white Gaussian noise (AWGN), binary random, and burst erasure channels with iterative decoding in terms of bit-error probability, block-error probability, error-floor, and rate of decoding convergence, collectively. Particularly, they have low error floors. Since the codes are QC, they can be encoded using simple shift registers with linear complexity.
24767232
The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System
This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.
18717814
Research on Distributed Data Mining Tool Used in Control System
In this paper, A distributed data mining tool that suitable for distributed and isomeric database environment of intelligent control system by borrowing ideas from architecture of grid is introduced. It can also perform incremental data mining based on history mining result. This tool makes full use of Metadata of each kind of resources. The resources associated in the data mining are all packed into form of web service, which is give a description by respective attribute document of the XML format. SOAP takes charge of the communication between the separated parts of the whole data mining process.
6477031
Universal approximation using incremental constructive feedforward networks with random hidden nodes
According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g : R --> R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g : R --> R and integral of R g(x)dx not equal to 0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.