Su-Chong Joo

Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution

In this paper, we introduce an advanced architecture of K-means clustering-based polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) and develop a comprehensive design methodology supporting their construction. The architecture of the p-RBF NNs comes as a result of a synergistic usage of the evolutionary optimization-driven hybrid tools. The connections (weights) of the proposed p-RBF NNs being of a certain functional character and are realized by considering four types of polynomials. In order to design the optimized p-RBF NNs, a prototype (center value) of each receptive field is determined by running the K-means clustering algorithm and then a prototype and a spread of the corresponding receptive field are further optimized through running Particle Swarm Optimization (PSO) and Differential Evolution (DE). The Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of the polynomials (which serve as functional connections of the network). The performance of the proposed model and the comparative analysis involving models designed with the aid of PSO and DE are presented in case of a nonlinear function and two Machine Learning (ML) datasets

TMO-Based Object Group Framework for Supporting Distributed Object Management and Real-Time Services

In this paper, we present a TMO-based object group framework that can support the distributed object group management and the real-time scheduling services on distributed real-time computing environments. These environments have some difficulties for managing lots of distributed objects and providing the timing constraints to real-time objects. For simultaneously solving these problems, we design a TMO object group framework that can manage as a grouping unit of the distributed TMO objects in order to reduce their own complicated managements and interfaces among individual objects without modifying the ORB itself. The TMO object as real-time object, defines the object having real-time property developed from Dream Laboratory at UC at Irvine. The TMO object group we suggested contains several components reflected the object grouping concepts and real-time service requirements analyzed by referring OMG CORBA specifications. To construct our TMO object group framework, we designed the TMO object group structure, and described the functional class diagram with representing relationships among components. We also explained the detailed functional definitions and interactions between the components from the following 2 points of views; object management service by the Dynamic Binder object for selecting an appropriate one out of objects with the same property, and the real-time scheduling service by the Scheduler object and the Real-Time Manager object. We finally verified the results produced by using the known algorithms like the Binding Priority algorithm and the EDF algorithm to see whether a distributed object management service and a real-time service can adapt on the suggested framework.

Identification of Fuzzy Inference System Based on Information Granulation

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with “conventional” evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature.

Construction of Distributed Object Group framework and its execution analysis using Distributed application simulation

The Distributed Object Group Framework(DOGF) we constructed supports the grouping of distributed objects that are required for distributed application. From the DOGF, we manage distributed application as a logical single view by applying the concept of object group, therefore the framework can provide distributed transparency for client’ request and binding service between/among objects. The DOGF also has an adaptive structure for supporting given application’s features. For that reason, we adapted the object registry policy, the security policy, the load balancing policy, and the real-time policy to our framework. For using practically the DOGF on distributed environment, we developed the Defence System against Invading Enemy Planes(DSIEP) as a simulation program that is the distributed application with real-time properties. From all of results obtained via the DSIEP simulation on the DOGF, we verified whether the DOGF supports the adaptability of distributed services and the functionality of group management.

Mobile Collaboration Framework for u-Healthcare Agent Services and Its Application Using PDAs

In this paper, we suggested a mobile collaboration framework based on distributed object group framework (DOGF). This paper focuses on the use of this framework to support mobile collaboration. Therefore, we improved the existing work and apply it to the construction of u-healthcare agent services. For supporting mobile collaboration, we divided into two agent types such as the stationary and the moving-typed agents according to the function of mobile devices. The data collected by sensors attached on arbitrary spaces can be shared by 2-typed agents or a home server, and exchanged with each other using the Push and Pull methods. For managing this information, the DOGF provides functions of object group management, storing information and security services to our mobile collaboration framework via defined application interfaces. The agent for executing service functions of mobile devices and an integrated monitoring system on home server are implemented by using TMO scheme. And we used the TMOSM for interactions between distributed components. Finally, we showed via GUI the executablility of healthcare application supporting for medical work in hospitals on our mobile collaboration framework.

GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems

In this paper, we introduce the architecture of Genetic Algorithm (GA) based Feed-forward Polynomial Neural Networks (PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes (PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System (MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

The development of fuzzy radial basis function neural networks based on the concept of information ambiguity

There is a remarkably rich landscape of fuzzy clustering and ensuing design procedures of information granules. In a nutshell, fuzzy clustering (and clustering, in general) leads to direction-free constructs meaning that there is no clear distinction between input and output variables. In the framework of fuzzy modeling, information granules are used in the development of input-output mapping and from this perspective it becomes beneficial to consider the aspect of directionality in the construction of information granules (fuzzy sets) in the input space. Conditional fuzzy C-means clustering comes as one of the algorithmically viable alternatives using which we construct fuzzy sets over the input space in presence of supervision coming in the form of structure of data distributed over the output space. In this paper, presented is a new clustering method in which we use the ambiguity index to express the boundaries of the clusters. The design is illustrated with the aid of several numeric examples that provide a detailed insight into the performance of the fuzzy models formed in this manner and also highlight several crucial design issues.

Location-Aware data mining for mobile users based on neuro-fuzzy system

Data mining tools generally deal with highly structured and precise data. However, classical methods fail to handle imprecise or uncertain information. This paper proposes a neuro-fuzzy data mining approach which provides a means to deal with the uncertainty of data. This presents a location-based service collaboration framework and uses the neuro-fuzzy algorithm for data mining. It also introduces the user-profile frequency count (UFC) function to determine the relevance of the information to mobile users. The result of using neuro-fuzzy system provides comprehensive and highly accurate rules.

Tourism guided information system for location-based services

Mobile information community develops quickly, as mobile telecommunication technology matches to the third generation. XML-based GIS becomes a global standard and the foundation. Recent developed Geography Markup Language (GML) allows integration of GIS location-based services, telematics, and intelligent transportation systems. In this paper, we propose a tourism information system for supporting the location based service of GIS applications. The system implements thin-client/server technology for mobile Web mapping service. The system includes traditional GIS system for navigation service and location finder POI services. The system for location and POI determination with design concerns are presented. An experimental user interface of PDA within the system is illustrated for the system procedures.

Development of a Mini-Mobile Digital Radiography System by Using Wireless Smart Devices

The current technologies that trend in digital radiology (DR) are toward systems using portable smart mobile as patient-centered care. We aimed to develop a mini-mobile DR system by using smart devices for wireless connection into medical information systems. We developed a mini-mobile DR system consisting of an X-ray source and a Complementary Metal–Oxide Semiconductor (CMOS) sensor based on a flat panel detector for small-field diagnostics in patients. It is used instead of the systems that are difficult to perform with a fixed traditional device. We also designed a method for embedded systems in the development of portable DR systems. The external interface used the fast and stable IEEE 802.11n wireless protocol, and we adapted the device for connections with Picture Archiving and Communication System (PACS) and smart devices. The smart device could display images on an external monitor other than the monitor in the DR system. The communication modules, main control board, and external interface supporting smart devices were implemented. Further, a smart viewer based on the external interface was developed to display image files on various smart devices. In addition, the advantage of operators is to reduce radiation dose when using remote smart devices. It is integrated with smart devices that can provide X-ray imaging services anywhere. With this technology, it can permit image observation on a smart device from a remote location by connecting to the external interface. We evaluated the response time of the mini-mobile DR system to compare to mobile PACS. The experimental results show that our system outperforms conventional mobile PACS in this regard.