Applied Mechanics and Materials Vols. 284-287

Abstract: This In this paper, a specific system is developed to recognize images of flower types. The proposed automatic flower boundary extraction method consists of two major procedures: the detection of four edge points and boundary tracing. Flower recognition includes two stages: feature extraction and matching. For the flower boundary extraction portion, we present a new technique for automatically identifying a flower’s boundary in an image. For boundary tracing, an intelligent scissors algorithm is applied. The color gradient magnitude cost term is implemented so that it can act directly on the three components of the color image. Suggested extraction of the characteristics has used division of the image in three levels (level 1, level 2, and level 3), the RGB and YCbCr of each level, the minimum Euclidean distance value of eight colors, and the number of petals. Using multi-class SVM, this dissertation derived 97.07% recognition of thirteen different types of flower images.

Abstract: In this paper, a novel fuzzy measure, high order lambda measure, was proposed, based on the Choquet integral with respect to this new measure, a novel composition forecasting model which composed the GM(1,1) forecasting model, the time series model and the exponential smoothing model was also proposed. For evaluating the efficiency of this improved composition forecasting model, an experiment with a real data by using the 5 fold cross validation mean square error was conducted. The performances of Choquet integral composition forecasting model with the P-measure, Lambda-measure, L-measure and high order lambda measure, respectively, a ridge regression composition forecasting model and a multiple linear regression composition forecasting model and the traditional linear weighted composition forecasting model were compared. The experimental results showed that the Choquet integral composition forecasting model with respect to the high order lambda measure has the best performance.

Abstract: The high accuracy time-frequency representation of non-stationary signals is one of the key researches in seismic signal analysis. Low-frequency part of the seismic data often has a higher frequency resolution, on the contrary it tends to have lower frequency resolution in the high frequency part. It’s difficult to fine characterize the time-frequency variation of non-stationary seismic signals by conventional time-frequency analysis methods due to the limitation of the window function. Therefore based on the Ricker wavelet, we put forward the matching pursuit seismic trace decomposition method. It decomposes the seismic records into a series of single component atoms with different centre time, dominant frequency and energy, by making use of the Wigner-Ville distribution, has the time-frequency resolution of seismic signal reach the limiting resolution of the uncertainty principle and skillfully avoid the impact of interference terms in conventional Wigner-Ville distribution.

Abstract: This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. It was developed to analyse and access the quality of human limbs motion that can be used in hospitals, clinics and human motion researches. An experiment was set up in a laboratory environment with conjunction of analysing human motion and its behaviour. The instruments demonstrate adequate internal consistency of results as below: 1. Compass sensor gives a better result with less standard deviation values especially in x-axis according descriptive statistical data. 2. Compass sensor gives a clearer scatter plot for better classification. 3. R2 (amount of variation explained) for sensor attached on arm is lower than hip and that means data collected from this site have a consistent trend. A

Abstract: This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. It was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum scatter plot in gyroscope and accelerometer for pattern classification. PCA used in this study was successfully differentiate and classify

Abstract: Image segmentation is an important part of the image processing. Currently, image segmentation methods are mainly the threshold-based segmentation method, the region-based segmentation method, the edge-based segmentation method and the Snake model based on energy function etc. This paper presents a novel image segmentation method based on the Poisson equation. The goal of the segmentation method is to divide the image into two homogeneous parts, the boundary portion and the non-boundary portion, which have similar gray values in homogeneous part. The key of the method is to build a Poisson equation with Dirichlet boundary condition. It sets a gradient threshold as the Dirichlet boundary condition of the Poisson equation, and gets a binary image by retaining the image boundary and smoothing the non-image boundary. Then simple binary segmentation will be able to get the image boundary. The experimental results show that this segmentation method can get accurate image boundaries for non-noise images and the weak noise images.

Abstract: Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.

Abstract: Video summarization aims at providing compact representation containing enough information for users to understand the entire content or important events, which serves as the fundamental process in content-based video analysis. This paper presents a novel sport video summarization algorithm by mining consistent field-of-views applied visual and temporal information in a totally unsurprised manner. After videos are broken into shots, a content-based similarity measure is proposed in the shot level to structurally analysis the visually matching cost of original videos. Then modified agglomerative hierarchical clustering is performed with an energy-based function to match the statistical distribution of various views in game videos and a refined distance metric is proposed as similarity measure of two shots. Extended temporal prior is introduced to meet the fact that temporally neighbored shots with similar duration are more likely to be in the same clusters. Experiments on a database of 6 sport genres with over 10251 minutes of videos from different sources achieved an average accuracy of 91.5% and quantitative results are presented to justify each choice made in the design of our algorithm. Our proposed algorithm is applied for the non-linear browsing service of Orangesports by France Telecomm and an android based app has been implemented for smart mobile devices.

Abstract: The main issue of the Q-matrix theory for cognition diagnosis is how to find the reduced Q-matrix containing the all efficient items. In this paper, based on the attribute structure matrix transformation, a novel recognition function for an efficient item vector is proposed. Two fast algorithms, transformation algorithm and expansion algorithm for finding the reduced Q-matrix are proposed as well. Some important properties are also discussed.

Abstract: In this research, we propose a Foraging_PSO algorithm, relative to the real competitive environment, to apply particle swarm optimization (PSO) algorithm in dynamic foraging game to solve the vehicle routing problem with time windows (VRPTW). Meanwhile, under the hypothesis of group decision making in a foraging swarm and the hypothesis of each forager also is a predator of the other foragers, through decision selection in this foraging game, an analytical decision process can be obtained to support decision making.