Applied Mechanics and Materials Vols. 479-480

Abstract: This study aims to probe into causal relationship among serious leisure traits, leisure internal motivation, leisure benefits and sense of happiness and further examines the moderating roles of leisure internal motivation and leisure benefits. Research design is based on questionnaire survey. Bicyclists of 2009 Giant Cup, 2009 Mountain Tour and Sea Appreciation, Challenge 100 cycling in Miaoli and 2009 Taichung 100K Cycling Race are treated as the subjects. The researcher conducts investigation on the fields by convenience sampling and acquires 500 valid samples. According to research findings, serious leisure traits positively influence leisure internal motivation, leisure benefits and sense of happiness. Leisure internal motivation positively influences leisure benefits and sense of happiness. Leisure benefits positively influence sense of happiness. Leisure internal motivation and leisure benefits play partial moderating roles between serious leisure traits and sense of happiness. Overall explained power of serious leisure traits, leisure internal motivation and leisure benefits on sense of happiness is 88%. Research findings aim to serve as reference for governmental departments in the promotion of bicycle activities, private bicycle groups or firms strategies of bicycle activities and market expansion and future academic studies.

Abstract: This paper uses the mathematical software Maple for the auxiliary tool to study the partial differential problem of two types of multivariable functions. We can obtain the infinite series forms of any order partial derivatives of these two types of multivariable functions by using differentiation term by term theorem, and hence greatly reduce the difficulty of calculating their higher order partial derivative values. On the other hand, we propose two examples of multivariable functions to evaluate their any order partial derivatives, and some of their higher order partial derivative values practically. At the same time, we employ Maple to calculate the approximations of these higher order partial derivative values and their infinite series forms for verifying our answers.

Abstract: Recently, heterogeneous system architectures are becoming a mainstream for achieving high performance and power efficiency. In particular, many-core graphics processing units (GPUs) have started to play an important role for computing in heterogeneous architectures. However, for application designers, computational workload still needs to be distributed among heterogeneous GPUs manually and remains inefficient. In this work, we propose a MINLP-based method for efficient workload distribution among GPUs by considering the capabilities of GPUs for various applications. Experimental results demonstrate the performance of our proposed method.

Abstract: Abstract. The problem of fingerprint classification is discussed for many years. Support Vector Machine (SVM) is a traditional artificial intelligence algorithm developed for dealing classification problems. In this paper, we used the idea of multi-objective optimization to transform SVM into a new form, since the core concept of SVM is built up on a single optimization equation, and some parameters for this algorithm still need user to make tons of experiment to determine. Our algorithm has successfully proved that user do not need to make experiment to determine the penalty parameter C. NIST-4 database is used to assess our proposed algorithm. The experiment results show our method can get good classification results.

Abstract: Physical inactivity is becoming a major public health concern and lead to a variety of chronic diseases. Since adequate moderate or vigorous activity can reduce the incidence of chronic diseases, noncommunicable disease and obesity. The evidence is supporting the importance of physical activity on health and well-being. However, many people nowadays live without adequate physical activity, and do not aware whether their daily activity is enough or not. The activity recognition and activity level can be used to survey the effectiveness and achievement of goals aimed at increasing physical activity. Physical activity monitoring has become a more proactive healthcare service that should build on the real-time reminding offered by healthcare solutions. Therefore, physical activity monitoring and activity level assessment are critical to maintain adequate physical activity and improve health. In this work, we present a motion patterns analysis for physical activity recognition and activity level assessment by using a wearable sensor. The proposed mechanism uses triaxial accelerometer as a sensing device. The sensor node is mounted in the right front waist, sensing and transmitting sensing data to server. The time series of raw data will be preprocessed through the aggregation technique of jumping window. The raw data will be divided into small segments and separated to gravity signal and body acceleration by filter. Through feature extraction and proposed classifier, motion pattern analysis is achieved. The classifier consists of activity recognition and activity level assessment algorithms. The results have demonstrated that the proposed methods can achieve 94.7%, 87.0% accuracy of activity recognition and activity level estimation respectively.

Abstract: This study uses the mathematical software Maple for the auxiliary tool to evaluate two types of definite integrals. We can determine the closed forms of these two types of definite integrals by using Leibniz differential rule and differentiation with respect to a parameter. At the same time, we provide some definite integrals to do calculation practically. The research methods adopted in this study involved finding solutions through manual calculations and verifying these solutions by using Maple. This type of research method not only allows the discovery of calculation errors, but also helps modify the original directions of thinking from manual and Maple calculations. Therefore, Maple provides insights and guidance regarding problem-solving methods.

Abstract: This paper uses the mathematical software Maple for the auxiliary tool to study the evaluation of two types of multiple improper integrals. We can obtain the infinite series forms of these two types of multiple improper integrals, and we propose four examples to do calculation practically. The research methods adopted in this study involved finding solutions through manual calculations and verifying these solutions by using Maple. This type of research method not only allows the discovery of calculation errors, but also helps modify the original directions of thinking from manual and Maple calculations. For this reason, Maple provides insights and guidance regarding problem-solving methods.

Abstract: The advancement in computer technology provided instant messaging software that makes human interactions possible and dynamic. However, such software cannot convey actual emotions and lack a realistic depiction of feelings. Instant messaging will be more interesting if users’ facial images are integrated into a virtual portrait that can automatically create images with different expressions. This study uses triangular segmentation to generate facial expressions. The application of an image editing technique is introduced to automatically create images with expressions from an expressionless facial image. The probable facial regions are separated from the background of the facial image through skin segmentation and noise filtration morphology. The control points of feature shapes are marked on the image to create facial expressions. Triangular segmentation, image correction, and image interpolation technique are applied. Image processing technology is also used to transform the space of features, thus generating a new expression.

Abstract: t has been proposed in this paper an idea of refining depth map obtained according to local stereo matching. Energy was calculated based on the entire image, meanwhile, energy minimization concept was adopted, and the area obtained according to color segmentation algorithm was adopted too. The lower the energy of an image, the better depth quality will be generated. The color feature and depth value among different regions and their neighboring regions are used to define the relation between the smooth and occluded regions in the energy function. Then the region energy was calculated repeatedly until the change was insignificant or the number of iterations was reached. The corrected left and right view was used first to perform local stereo matching to get initial depth estimation. The color information of the left view was used to perform color segmentation, and then the segmented region and initial depth estimation were used to calculate the parameter of disparity plane for each region. This process was performed iteratively on the disparity plane, where a more reasonable depth map can be obtained while the energy cost is minimized. From the experimental result, it is proved that the depth map after refinement showed better object shape and smooth region density as compared to that of the initial depth map.

Abstract: A novel near space target optical properties modeling method based on the reverse Monte Carlo optical tracking is proposed. Both the environment characteristics in near space and the light transmission characteristics in different detection platform mode are considered and analysis in detail. The invalid light can be eliminated adaptively in this model. According to the phase relationship of target-observing systems-the sun, the optical properties of the target can be obtained. Some numerical simulations are made to test the accuracy and capability of the proposed modeling method.