Earticle

Since the resource of nodes can access and leave the Grid system freely, it is possible some malicious nodes exist in the Grid environment. In addition, the numbers and qualities of nodes’ resource can be changed dramatically and optionally, so these malicious nodes must be affecting the utilization of Grid resource. Hence, the trust management mechanisms are developed and widely used in the resource scheduling for Grid environments. However, most of the trust management models are mainly focus on the nodes historic behaviors and few of them consider the future trust of the node based on its previous activities. This paper proposes a new method to detect the nodes behaviors of resource providers in Grid environments based on D-S theory. Through simulating experiments, the new mechanism could record the behaviors exactly and prevent the malicious ones accessing the Grid system effectively. It would give a strong support for the success of resource scheduling in Grid environments.

In this paper, we propose a framework for recognizing human activities in our daily life. Since a human activity is represented as a sequence of actions, the actions are recognized from videos and then the frequently-occurring human activities can be extracted from them. We show the experimental results applied to the data taken in a deskwork environment to demonstrate the performance of the proposed framework. The experimental results were as follows: 86.0% averaged recall rate and 78.3% averaged precision rate were obtained in extracting human activities.

The route guidance system, which provides driving advice based on traffic information about an origin and a destination, has become very popular along with the advancement of handheld devices and the global position system. Since the accuracy and efficiency of route guidance depend on the accuracy of the traffic conditions, the route guidance system needs to include more variables in calculation, such as real time traffic flows and allowable vehicle speeds. As variables considered by the route guidance system increase, the cost to compute multiplies. Since handheld devices have limited resources, it is not feasible to use them to compute the exact optimal solutions in real time systems by some well-known algorithm, such as the Dijkstra’s algorithm, which is usually used to find the shortest path with a map of reasonable numbers of vertices. To solve this problem, we propose to use the genetic algorithm to alleviate the rising computational cost. We use the genetic algorithm to find the shortest time in driving with diverse scenarios of real traffic conditions and varying vehicle speeds. The effectiveness of the genetic algorithm is clearly demonstrated when applied on a real map of modern city with very large vertex numbers.

This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs that could optimize two objectives in a single run; (1) maximize the mobile robot homing behavior whilst (2) minimize the hidden neurons involved in the feed-forward ANN. The generated controllers are evaluated on its performances based on Pareto analysis. Furthermore, the generated controllers are tested with four different environments particularly for robustness assessment. The testing environments are different from the environment in which evolution was conducted. Interestingly however, the testing results showed some of the mobile robots are still robust to the testing environments. The controllers allowed the robots to home in towards the signal source with different movements’ behaviors. This study has thus revealed that the PDE-EMO algorithm can be practically used to automatically generate robust controllers for RF-localization behavior in autonomous mobile robots.

To provide services with a concept of anywhere, anytime, anyone, anything, and any device, ubiquitous and pervasive computing (UPC) becomes a popular paradigm. The purpose of UPC is to emerge computers into the real world to serve humans and the underneath infrastructure of UPC is called ubiquitous networks. In order to provide ubiquitous services (u-Service) which deliver useful information to service users without human intervention, this paper implements a proactive information delivery system using Bluetooth technology. Bluetooth is a low-powered networking service that supports several protocol profiles, most importantly file transfer. Combined together, ubiquitous computing and Bluetooth have the potential to furnish ubiquitous solutions (u-Solutions) that are efficient, employ simplified design characteristics, and collaboratively perform functions they are otherwise not capable. Thus, this paper first addresses the current Bluetooth technology. Then, it suggests and develops the proactive information delivery system utilizing Bluetooth and ubiquitous computing network concepts. The proactive information delivery system can be used in many ubiquitous applications such as ubiquitous commerce (u-Commerce) and ubiquitous education (u-Education).

The rapid advances in communication, information technology, and network technologies have had key role in emerging heterogeneous large-scale distributed systems. Besides, emergence of enterprise modeling and high integration of virtual organizations, proffesional virtual communities, and growth of number of practical cases on different forms of collaborative networks represent major trends and challenges in managing these systems. In a virtual organization, managing partners is crucial. An Autonomous System improves virtual organization functionalities and cope with complexity managemnt. Purpose of this paper is to present autonomous virtual organizations describing their characteristics, effects on quality factors, their building blocks architecture, and challenges. It then outlines this autonomous model for a virtual organization environment.

The accurate detection of QRS complexes is important for ECG signal analysis. This paper presents an improved version of a QRS detector based on an adaptive quantized threshold. The algorithm achieves high detection rates by using automatic thresholds instead of predetermined static thresholds. We improved the number of detected QRS in non-stationary random arrhythmic ECG signals by applying a secondary threshold. The performance of the algorithm was tested on 19 records of the MIT/BIH Arrhythmia Database resulting in 97.5% sensitivity and 99.9% positive predictivity.

Based on researching parameter optimization method of neural network deeply, a new parameter optimization method is presented and applied to surface defect online inspection system of cold rolled strips. The method takes advantages of small-samples fully, and can get a group of neural network parameters which can mostly express the neural network under a certain specific condition. The method is advantageous for its simplicity, easy to maintain and fast, it can be applied to many fields too, such as iron-steel industry, medicine. Experiments showed that a best recognition effect by using the parameters for neural network which are achieved by the new parameter optimization method can be got among all the parameters optimized randomly for surface defect of cold rolled strips.

We predict accuracy of user’s preferences by using memory-based collaborative filtering algorithm in recommender system, and then analyze the results through the EDA approach. The possibilities are presented that prediction accuracy can be evaluated before prediction process by analyzing the results. The classification functions using the generative probability of specific ratings are made, and users are classified by using the classification functions. The prediction accuracies of each classified group are analyzed through statistical tests. The method of setting the Error Bound of users who have high probabilities in low prediction accuracy will be presented.

Recent years, China has become one of the biggest market for International IT product giants have entered China market in recent years, such as MSN, Google, eBay, and Yahoo!. All of these products experience fierce competitions from indigenous IT products. Although indigenous IT products provide almost the same function as their international competitors, they dominate the market after passing the phase of imitation. What are the key reasons for their success? In this paper, we compare the detailed designs of MSN vs. QQ. By proposing a conceptual model, we investigate how the different functions of MSN and QQ lead to different users’ adoption intention. The conceptual model is not only helpful for researchers who are keen to investigate the factors which affect IT adoption process, but also shed light on how to understand users behavior and grab the market share for practitioners in China.