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This paper introduces a novel AI framework for nationwide wireless KPI monitoring and its diagnosis in LTE/5G mobile networks. The proposed method, which has not been provided so far for a commercial use, automatically detects meaningful degradations of cell-specific wireless KPI statistics and also provides an alarm to administrator with possible causes of the corresponding problem. Machine learning and deep learning technologies are properly applied to detect relative KPI degradations on the basis of those estimated globa/local PDFs and also to carry out root cause analysis for the problem diagnosis, respectively. We have confirmed the feasibility of the mentioned AI framework by completing a first PoC through interworking our AI server with commercial LTE/5G networks. Currently, about 20 major monitoring KPIs such as RRE, ENDC,RSSI, BLER, and so on are being considered in current 5G NSA mode.

A big data is now widely utilized in real-world situations. And the business field requires some cooperation or coupling between different big data systems. However, the big data system does not have a unified format and environments. Thus, this paper presents an agent-based flexible management for big data fusion services. The proposed system utilizes an Internet Relay Chat (IRC) and a concept of IRC-bots. It may provide efficient data fusion services without a concern about the heterogeneous system.

Grid technology enables to share and use of various heterogeneous resources. However, it is very difficult to satisfy user requirements and benefits of resource providers. This paper proposes an ontology-based resource management model (ORMM). The model builds an ontology of ORMM for computational grid. As using the ontology and the inference based on some rules, we can reflect requirements of resource users, conclude more transactions, and reduce resting resources. To evaluate performances, the number of transactions, transaction period, and cumulative profits are measured. In experiments, ORMM increases about 32% of greater transactions, reduces about 40% of transaction period. Experimental results demonstrate that the proposed model can improve the efficiency of resource management in the computational grid environment.

Recently, a lot of boosting-based feature selection methods have been introduced. However, the effective methods that can select more discriminative features at lower computational cost are needed. In this paper, we propose a novel boosting algorithm, called the Segment-Boost, and describe an improved feature selection method based on Segment-Boost. Segment-Boost learns the weak classifiers for the feature sets of various sizes through given training examples. Hence, our proposed feature selection method can consider discriminations and complementarities of features for selecting features. In addition, the proposed feature selection method requires lower computational cost than other boosting-based feature selection methods. For experiments, we extracted Gabor feature vectors from 400 face images of ORL facial database through Gabor filters. And randomly generated intra/extra-personal feature vectors from the Gabor feature vectors were used for training and testing examples. From the experimental results, we verified that Segment-Boost can select more discriminative features at lower computational cost than other boosting-based feature selection methods.

Grid computing has been noticed as a solution for digital content management. But, existing grid computing-based digital content management systems cannot meet complex and detailed requirements of a grid user due to the simple search mechanism base on few keywords. Therefore, this paper constructs a digital content ontology that represents digital content information. And, this paper proposes a semantic Web-based digital content management system. In the system, a semantic Web agent analyzes user requirements and infers a proper digital content from the digital content ontology. And, the system selects a grid resource with the cheapest and fastest data service among grid resources which can provide the inferred digital content. Digital content data is transferred from the selected grid resource to the grid user through a local grid resource agent.

WiBro (Mobile WiMAX) is a broadband wireless internet service supporting for fixed, portable and mobile users, but enhancement of service quality in indoor environment is still necessary. WiBro femtocell is developed to expand cell coverage, increase system capacity and offer various multimedia service with high throughput in indoor environment. Three types of femtocell are designed according to the class of application environments, such as home, SOHO and enterprise. The functionalities of radio access station and access control router are integrated in Home and SOHO femtocell. Inter-operational test with mobile terminal and performance evaluation are carried out and research results on related technical issues such as synchronization and auto-configuration are also described.

As demand and supply of Grid resources in Grid computing environments increases rapidly, many Grid researchers have taken particular interest in efficient management of Grid resources. In this paper, a user demand prediction-based resource management model (UDPRMM), which uses the user demand trend of Grid resources as a critical management factor, is proposed. This paper introduces a user demand prediction approach with historical user demands and aims for effective Grid resource management through transaction management between resource users and resource suppliers. For performance evaluation with accuracy and reliability, the UDPRMM is compared with the user demand fixed-based resources management model (UDFRMM) and the user demand random-based resources management model (UDRRMM). The UDPRMM demonstrates improvement greater than 70% with regard Grid resource transactions and greater than 16% with regard to Grid resource transaction quantities in comparison with the UDFRMM and the UDRRMM. In evaluation, the UDPRMM reduces delay time by 21%, over that of the UDFRMM, and 19% over that of the UDRRMM. The empirical results demonstrate the usefulness of the UDPRMM with effective economics-based resource management.

The mobile grid introduces various research challenges distinguished from existing grid computing systems. They are low bandwidth, low processing power, low battery capacity, frequent disconnectivity, and mobility. Mobility of the grid node increases the system load of the mobile grid in a constrained operating environment by increasing the number of communication messages required to confirm the location between the grid broker and mobile grid node. Therefore, this paper proposes an adaptive distance filter that can effectively reduce communication traffic between the mobile grid node and grid broker. This filter constructs clusters based on the mobility and velocity of the grid node and filters the location updates. However, the reduction of location updates generates location errors, which occur when the grid broker cannot acquire the exact location of mobile nodes. To solve this problem, if the location updates are filtered, the grid broker can estimate the location of the mobile node using a statistical estimation method. For the performance evaluation of the adaptive distance filter, we modeled the mobility of the grid nodes. We then measured the reduction in location updates and location errors. In these experiments, we prove that the adaptive distance filter is an effective scheme for reducing location updates and the grid broker can reduce location errors through location estimation.

This paper deals with congestion control in which GRID network community has been interested. In order to solve problems of existing congestion control models like FOBS, this paper proposes the node availability-based congestion control model. The model monitors not only storage availability but also network availability of grid nodes to detect and control congestion. As using the fuzzy adaptive packet allocation mechanism, the model estimates congestion level of grid nodes and adjusts their packet transmission rate for stable and reliable data transfer in computational grid. Experiment results demonstrate that our model outperforms FOBS which is a typical congestion control model with the improved QoS.

Communication Data Management (CDM) is an important issue in complex and large-scale modern geo-computation applications. In this paper, we review existing CDM schemes and propose an efficient adaptive quantization-based CDM scheme for geocomputation in grid environment. This model is based on theory of quantization and uses distributed clusters which are concerned about the features and geographical adjacency of communication objects. And, this model uses various thresholds which are suitable for individual clusters and effectively reduces communication messages by clustering and diverse thresholds. For realization of this scheme, we design and develop a HLA (High Level Architecture)-based system for geo-computation in grid environment. For performance evaluation, this paper measures reduction rate of message traffic and location errors. This scheme has more effective message traffic reduction and less location errors with precise location accuracy rate than general quantization-based scheme. The empirical result apparently presents that adaptive quantization- based CDM scheme is a suitable scheme for high-performance geo-computation.

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 proposes a grid-based intelligence ART1 classifier which operates an ART1 clustering classification using grid computational resources with distributed GPCR data sets. This paper evaluates performance of the grid-based ART1 classifier in comparing to the grid-based ART1 classifier and the grid-based 3-tier ART1 classifier. The number of communication message of the grid-based intelligence ART1 classifier is less than the others classifier. And the classification processing time of the grid-based intelligence ART1 classifier is 13% classification processing time of the grid-based ART1 classifier and is 25% classification processing time of the grid-based 3-tier ART1 classifier. Computational grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets and provides a good progress in guaranteeing high bioData accuracy with reasonable processing resources

A 1.8 V 1 Gb flash memory uses a 0.12 /spl mu/m STI process technology. A charge pump operates at

A 1.8 V 2 Gb NAND flash memory is fabricated in a 90 nm process resulting in a 141 mm/sup 2/ die and a 0.044 /spl mu/m/sup 2/ effective cell. To achieve the high level of integration, critical layers are patterned with KF photolithography and phase-shift masks with proximity correction.

The VORAM (vehicle for ocean research and monitoring) autonomous underwater vehicle (AUV), is developed in KRISO (Korea Research Institute of Ships and Ocean engineering) for the observation and investigation of seabed. This paper describes the design and configuration of the AUV system and presents a discrete-time quasi-sliding mode controller with parameter uncertainties and long sampling interval. The proposed controller makes the system stable in the presence of system uncertainties and even external disturbances. Depth keeping control and contouring control are performed for a numerical model of the AUV with full nonlinear equations of motion to verify the effectiveness of the proposed control schemes when the vehicle has long sampling interval. The experimental results agree with the simulations. As sampling interval becomes large the effectiveness of the proposed control law is more prominent compared with the conventional sliding mode controller.

This paper describes the design and development of DEVS/GDDM, an environmental platform for data distribution management, that allows us to study the interestbased quantization scheme in order to achieve effective reduction of data communication in distributed simulation. After a brief review of interest-based quantization scheme, we discuss the implementation of this scheme as a layer above the DEVS/HLA modeling and simulation environment. This paper presents theoretical analysis for bandwidth utilization of the interest-based quantization scheme, especially with predictive and multiplexing extensions, and empirical results in ballistic missiles simulation executing on the DEVS/GDDM environment in NT networking platforms. The results indicate that the DEVS/GDDM environment is very effective and scalable due to reduced local computation demands and extremely favorable communication data reduction with a reasonably small potential for error.