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In this study we focus on the effect of the interaction between humans and device when emotion recognition smart device is used. We propose that emotion based smart device is the most effective device in interacting with human when it comes to user-centered device. Forty participants watched Smart TV with three different user interfaces; remote controller, gesture recognition, voice recognition system and emotional recognition system. When they used the TV with the remote controller and voice recognition system, gesture recognition, they were given interesting and sad contents and were to choose any contents they want to see within a limited time. With emotion recognition system, Fraunhofer IIS SHORETM demo software, participants’ facial expression was automatically detected and they were provided with contents according to their emotions. This research offers a new concept that emotion-based smart device that can interact with humans will be the most effective user interface in HCI. The study will discuss how people feel and how Smart TV will respond accordingly.

Grid computing has been noticed as an issue to solve complex problems of large-scale bioinformatics applications and helps to improve data accuracy and processing speed on multiple computation platforms. Outlier detection helps classification success rate high and makes processing time reduce. This paper focuses on a data clustering and classification method with outlier detection which is an important bioinformatics application in grid environment. This paper proposes a grid-based and outlier detection-based clustering and classification(GODDCC) using grid computational resources with geographically distributed bioinformatics data sets. This GODDCC is able to operate large-scale bioinformatics applications in guaranteeing high bio-data accuracy with reasonable grid resources. This paper evaluates performance of GODDCC in comparing to the data clustering and classification(DCC) without outlier detection. The average of processing time of the GODDCC model records the lowest processing time and provides the highest resources utilization than the other DCC models. The outlier detection method reduces processing time for DCC models with maintaining high classification success rate and grid computing gives a great promise of high performance processing with geographically distributed and large-scale bio-data sets in bioinformatics applications.

Algorithms and methods for analyzing large amounts of data are studied and developed. This paper presents a Data Mining (DM) method operated in grid computing environment. Because DM technology uses large amounts of data and requires costs to compute, utilizing and sharing computing data and resources are key issues in DM. Therefore, a Dynamic Load Balancing (DLB) algorithm and a decision range readjustment algorithm are proposed and applied to the Grid-based Data Mining (GDM) method. And we analyzed the average waiting time for learning and computing time. For a performance evaluation, the system execution time, computing time, and average waiting time for learning are measured. Experimental results show that GDM with the DLB method provides many advantages in terms of processing time and cost.

The intelligent agent works powerful jobs for handling system complexity and making systems more modular. Especially a reasoning agent is effective on organizing for decision-making process of systems. This paper introduces an Ontology-based Intelligent Agent for a Grid Resource Management System (OIAGRMS), which uses ontology reasoning to select a suitable resource supplier, is proposed. This paper focuses on effective grid resource management and the improvement of resource utilization through transaction management for the OIAGRMS. For performance evaluation with accuracy and reliability, the OIAGRMS is compared with the Prediction-based Agent for Grid Resource Management System(PAGRMS) and the Random-based Agent for Grid Resource Management System(RAGRMS). The OIAGRMS recorded over 90 percents trade success, but the PAGRMS and RAGRMS recorded less than a 90 percents trade success. In comparing of resource utilization rate, maximum deviation, standard deviation, the OIAGRMS were about 9.4 and 9.8 percents but the PAGRMS are about 22.9 and 16.3 percents, the RAGRMS were about 61.6 and 21.7 percents. The empirical results demonstrate the usefulness and improvement utilization with stable performances of the intelligent agent base on ontology reasoning in grid environment.

The communication traffic for continuous data aggregation and location update is the main reason of short-lived mobile sensor networks because the energy consumption of sensor nodes is caused by the transmission of messages. For effective communication traffic management in mobile sensor networks, this paper proposes an dynamic threshold control-based adaptive message filtering method which utilizes diverse node parameters reflecting characteristics of mobile nodes, such as mobility and battery availability. In the proposed filtering method, each sensor node in a sensor filed filters out messages based on its distance threshold. In order to adjust the distance threshold by the condition of each node adaptively and dynamically, fuzzy logic is applied to the proposed filtering method. Empirical results approve that the proposed filtering method achieves more improved performance than existing filtering methods.

The current RFID system is based on a distributed computing system to process a great volume of tag data. Therefore, RFID M/W should provide load balancing for consistent and high system performance. Load balancing is a technique to solve workload imbalance among edge M/Ws and improve scalability of RFID systems. In this paper, we propose a fuzzy logic control-based load balancing agent for distributed RFID systems. The agent predicts data volume of an edge M/W and evaluates workload of the edge M/W by fuzzy logic control. And, the agent acquires workload information of an edge M/W group to which the edge M/W belongs. Also, the agent adjusts over and under-loaded workload according to a dynamic RFID load balancing algorithm. Experimental results demonstrates that the fuzzy logic control-based load balancing agent provides 95% and 6.5% reductions in overload time and processor usage and 13.7% and 12.2% improvements in throughput and turn around time as compared the mobile agent-based load balancing system.

Currently network uses, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is an effective management system with a high throughput and a low response time designed and implemented based on a model and simulation based data engineering methodology. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.

This paper proposes the QLP-LBS (Quantization and Location Prediction-based LBS). This QLP-LBS system is based on quantization theory and uses statistical location prediction mechanism. This LBS applies the quantum range of quantization theory to each mobile user and reduces location update costs by comparing results between moving distance of mobile user and quantum range. But, this LBS system generates location errors from the quantization. In order to solve this problem, we apply statistical location prediction mechanism to LBS system. This prediction mechanism predicts location of mobile user using its historical path and decrease location errors by quantization and makes more reliable LBS system. For performance evaluation, this paper measures location accuracy and reduction rate of location update costs with various quantum ranges. This experiments show that QLP-LBS effectively reduces location update costs of LBS system. Also, QLP-LBS solves problem of location errors using location prediction mechanism which is problem of general quantized system. Therefore, QLP-LBS is solution for reduction of location update costs and has reliable location accuracy.

The key purpose of computational grid is how to allocate grid resources to grid applications effectively. Existing resource management models with centralized architecture like GRAM and nimrod-G broker are liable to generate communication overheads and bottleneck. Therefore, we propose the service agent-based resource management model using virtuali-zation for computational grid. Virtualization and agent-based system applied to our model is able to improve load imbalance of grid broker and reduce communication overheads. Experiment results demonstrate that the service agent-based resource management model decreases 25.8% more job latencies and 151.6% more communication messages than the centralized resource management model.

Grid computing provides high performance like a super computer through sharing and using distributed heterogeneous computing resources. Grid computing processing time and cost are widely varied since a grid user or a grid middleware can select a variety of distributed heterogeneous resources. Therefore, grid computing absolutely needs a grid resource management method and model. In this paper, we propose two types of resource management model with resource reliability. The first model is the idle resource supplement model. The point of the idle resource supplement model is that adds to idle resources when existing resources can not process jobs. The second model is the validity time designation model which considers grid users. This model processes jobs during validity time that is decided by grid users. This paper evaluates system performance such as utilization, job-loss rate and average turn-around time. And, we estimate experiment results of our models in comparison with those of existing models such as a random model and a round-robin model. The experiment results demonstrate that the two models based on resource reliability measurement improve resource utilization and provide reliable job processing. And, we expect that our proposed models improve grid computing QoS.

Protein sequence data have been revealed in current genome research and have been noticed in demand of classifier for new protein classification. This paper proposes the optimal clustering-based ART1 classifier for the GPCR data classification and processes the GPCR data classification. We focuses on a demand of optimal classifier system for protein sequence data classification. The optimal clustering-based ART1 classifier reduces processing cost for classification effectively. We compare classification success rate to those of Backpropagation Neural Network and SVM. In experimental result of the optimal clustering-based ART1 classifier, classification success rate of ClassA group is 99.7% and that of the others group is 96.6%. This result demonstrates that the optimal clustering-based ART1 classifier is useful to the GPCR data classification. The classification processing time of the optimal clustering-based ART1 classifier is the 27% less than that of the Backpropagation Neural Network and is the 39% less than that of the SVM in an optimal clustering rate which is 15%. And the classification processing time of the optimal clustering-based ART1 classifier is the 39% less than that of the optimal clustering-based ART1 classifier in a prediction success rate which is 96%. This result demonstrates that the optimal clustering-based ART1 classifier provides the high performance classification and the low processing cost in the GPCR data classification.

Computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. Computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets.

Importance and need of grid process scheduling have been increased in accordance with development of grid computing. In order to distribute and utilize grid processors efficiently, grid computing system needs scheduling policies that manage and schedule grid process. This paper reviews current scheduling policies and proposes an efficient scheduling model which is called the predictive process scheduling model. For efficient scheduling, this paper presents the processing time prediction algorithm to resolve problems of grid scheduling. The predictive process scheduling model predicts processing times of processors, allocates a job to a processor with minimum processing time, and minimizes overall system execution times. For performance evaluation, this paper measures turn-around time, job loss, throughput, and utilization. Empirical results show that the predictive process scheduling model operates with the less 69.5% of turn-around time and the less 76.4% of job loss and improves the more 119.6 % of throughput and the more 117.8% of utilization than those of existing scheduling models such as random scheduling and round-robin scheduling.

Complex and large-scale distributed systems are characterized by numerous interactive data communication among distributed components over network. This paper proposes a communication data multiplexing approach as an efficient message traffic reduction scheme. This approach encodes joint outputs of sender components into a single message and applies to a distributed system involving components moving and interacting in multi-dimensional space. For performance evaluation, this paper applies uses a projectile/missile case study with realistic multi-dimensional dynamics. This paper investigates variation of system accuracy and network bandwidth requirement, while a ratio of active components and a time granule are varied. Analytical and empirical data clearly indicate the advantages of multiplexing in saving communication resources in a large-scale distributed simulation. In addition, this paper discusses effectiveness and limitation of the multiplexing approach while considering the tradeoff between system accuracy and performance.

Grid computing is the next generation platform that maximizes computing performance of system as using numerous computing resources on computer networks. Grid computing not only shares computing resources but also efficiently utilizes available computing resources. Therefore, grid computing needs resource management policies that handle and manage grid computing resources. This paper reviews existing resource management models based on economic principles such as auction model and commodity market model. This paper proposes an efficient resource management model, which is called the grid resource trade network model, and presents its bidding algorithms for grid users and grid providers. The proposed model reduces idle resources, improves resource utilization, and provides effective resource distribution. Empirical results show usefulness and efficiency of the grid resource trade network model.

This paper presents a modeling of a distributed simulation system with a data management scheme. The scheme focuses on a distributed simulation concept, which is load balancing, suggests distribution of a different functionality to each distributed component, and assigns various degrees of communication and computation loads in each component. In addition, this paper introduces a design with an inter-federation communication on HLA-compliant distributed simulation. The design focuses on integration among multiple federations and constructs the larger distributed simulation system by suggesting a HLA bridge connection. The integration supports system simulation flexibility and scalability. This paper discusses design issues of a practical system with a HLA bridge for inter-federation communication. This paper analyzes and evaluates performance and scalability of the data management scheme with load balancing on distributed simulation, especially with inter-federation and inside-federation communication configurations. The analytical and empirical results on a heterogeneous OS distributed simulation show improvement of system performance and scalability by using data management and inter-federation communication.

This paper presents a design and development of a data distribution management modeling in computational grid. The modeling focuses on system performance improvement of a satellite cluster management system which uses an inter-federation communication in HLA (High Level Architecture)-compliant grid system. The inter-federation communication allows to execute a complex large-scale cluster management system with distributed satellites and share the dispersed data assets among satellite components collaboratively. In addition, this paper discusses the data distribution management modeling with centralized and decentralized approaches to improve system performance and scalability of satellite cluster management. The analytical and empirical results on the heterogeneous OS-based computational grid show usefulness of data distribution management and inter-federation communication in the satellite cluster management system.

This paper reviews existing data management schemes and presents a design and development of a data management scheme with load balancing in a distributed computing. This scheme defines a variety of degree of load balancing, maps each degree to each data management configuration, and reduces data traffic among distributed components. The scheme allows to share geographically dispersed data assets collaboratively and execute a complex large-scale distributed computing system including cooperating and distributed components with reasonable computation and communication resources. In addition, this paper introduces a HLA (High Level Architecture) bridge middleware environment for data communication among multiple federations. We analyze system performance and scalability with a variety of degree of load balancing configurations. The empirical result on a heterogeneous OS distributed system apparently presents advantages of the data management scheme with load balancing in terms of system performance and scalability.

There is an increasing demand to execute complex large-scale grid computing systems and to share dispersed data assets and computing resources collaboratively. To perform grid computing within reasonable communication and computation resources, this paper introduces a scalable data management approach and its framework. This paper presents a design and development of the framework which performs the scalable data management approach. For development of this framework, this paper uses a hierarchical and modular object-oriented technology supported by a system modeling theory and specification. The framework provides a superior solution to reduce data traffic loads and local computation demands with a practical application.

As analyses of system behavior and complexity through computer modeling and simulation have been growing, high performance modeling is noticed to handle behavior and complexity of modern large-scale systems. The high performance modeling focuses on high resolution representation of system and performance improvement. This paper presents a quantized system modeling as an effective high performance modeling. The quantized system modeling represents state resolution using state quantization and improves system performance through message filtering among system components. This paper realizes a practical quantization component which is based on both of Discrete Time System (DTS) and Discrete Event System (DES) and shows usefulness of quantized system in a variety of industrial applications. This paper models a real-world application, e.g. a space traveling system with the quantized system and evaluates system performance, accuracy, and scalability of the quantized system on DES-based modeling and simulation.

This paper describes a design and development of an intelligence balancing for communication data management in a grid computing. The intelligence balancing allows to execute a complex large-scale grid computing system and share dispersed data assets collaboratively. After a brief review of communication data management, we discuss the design issue of the intelligence balancing. We analyze system performance and scalability, especially with non-intelligence and intelligence-based configuration. The empirical result on the heterogeneous OS distributed system indicates the intelligence balancing is effective and scalable due to the use of communication data reduction.