Search

BACKGROUND As interest in posterior body contour has increased, there has been an upward trend in the number of gluteal augmentations performed. However, there has also been a proportional increase in postoperative complications and subsequent revision operations. Nevertheless, there are currently few studies or reports on secondary gluteal augmentation. The purpose of the present study was to identify methods to treat and prevent complications resulting from gluteal augmentation. METHODS In this study, the authors review 18 cases of secondary gluteal augmentation. New intramuscular pockets were dissected more deeply than were previous pockets. Capsulotomy or capsulectomy was applied to the deep capsules of the previous pockets to sufficiently expand the new pockets and soft tissue to cover the implants. After triple-antibiotic irrigation, implants were inserted using the "no-touch technique." Next, "hybrid gluteal augmentation" was performed by grafting autologous fat into the subcutaneous fat layer. RESULTS Among 18 cases of secondary gluteal augmentation performed, nine cases were performed by means of hybrid gluteal augmentation and seven cases were implant-only augmentations. Postoperative complications occurred in two cases. One case involved the hybrid technique (remaining rippling), and the other case involved implant-only augmentation (implant palpability). However, there was no need for surgical correction. CONCLUSION Secondary gluteal augmentation can be successfully performed using the hybrid technique if the gluteus maximus is accurately dissected, the capsules are appropriately managed, and infection is rigidly controlled.

In this paper, we propose a new signage technology in conjunction with the standard IoT platform, hub and trust information analysis system to provide more differentiated and intelligent smart signage services. It is possible to display personalized and optimized contents through various situation data analysis and trust data analysis, and to control the situation based on situational awareness.

Background Pathogens in the nasal cavity during nasal surgery could lead to a systemic infectious condition, such as bacteremia, nosocomial infection, or toxic shock syndrome. However, there is no research about the prevalence of nasal carriage in patients with nasal bone fracture. Methods This was a prospective, double-blind, randomized study about the rate of nasal carriage in 200 patients with nasal bone fracture in Korea. Nasal secretions were taken from both the middle nasal meatus and colonized. All analyses were carried out using SPSS software. Results Pathogens were identified in 178 of the 200 cases. Coagulase-negative staphylococci (CNS) were the most cultured bacteria in 127 (66.84%) of the 190 total patients after excluding 10 cases of contaminated samples, and methicillin-resistant coagulase-negative staphylococci (MRCNS) were found in 48 (25.26%). Staphylococcus aureus was the second most identified pathogen, found in 36 (18.95%), followed by 7 cases (3.68%) of methicillin-resistant Staphylococcus aureus (MRSA). The prevalence rate of MRSA in the females was higher than that in the males (RR=4.70; 95% CI, 1.09-20.18), but other demographic factors had no effect on the prevalence rate of MRSA and MRCNS. Conclusions The prevalence rate of these pathogens in patients with nasal bone fracture in Korea was similar to other reports. However, few studies have addressed the prevalence rate of CNS and MRCNS in accordance with risk factors or the change in prevalence according to specific prophylaxis against infectious complications. Additional research is needed on the potential connections between clinical factors and microbiological data.

Demand-side management program is important for future smart grid. Most of the DSM program developed in the past interact customer and utility company. We suggest the Factory Management System using Social Internet of Things. It is not interaction between customer and utility company but interaction between things and things. Therefore we propose a autonomous demand-side management system and a system architecture.

2.4GHz Industrial Scientific Medical(ISM) Band is shared by both WLAN and IEEE 802.15.4. Therefore, WLAN and IEEE 802.15.4 get interfered by each other where WLAN and IEEE 802.15.4 deploy locally. Especially, IEEE 802.15.4 devices are more influenced by the interference from LWAN devices because WLAN devices transmit data with higher power than IEEE 802.15.4 devices. In this paper, we proposed a possible algorithm for interference coordination in the Heterogeneous Network (HetNet) and optimized an important parameter, NAV, of the proposed mechanism. Our goal is to mitigate the interference problem and to make IEEE 802.15.4 work properly while minimizing the loss of WLAN by making PAN Coordinator conduct the role of both WLAN and IEEE 802.15.4 devices.

IT infrastructure is evolving in a way to use physical information for providing context-aware services to the users and if necessary, to put some actions on the physical world for reacting appropriately based on perception on the physical world. Ubiquitous sensor network (USN) environment consists of various types of sensors and actuators to perceive and communicate with physical world. USN middleware is the system which collects physical information from various sensors and controls physical world with heterogeneous actuators on behalf of USN services. ETRI has been researching and developing USN middleware and this paper intends to introduce the results and the ongoing project.

Temporal data mining is still one of important research topic since there are application areas that need knowledge from temporal data such as sequential patterns, similar time sequences, cyclic and temporal association rules, and so on. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from temporal interval data such as patient histories, purchaser histories, and web logs etc. We propose a new temporal data mining technique that can extract temporal interval relation rules from temporal interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of temporal interval data and a temporal relation algorithm for mining temporal relation rules from the generalized temporal interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.

With the increasing need for intelligent environment monitoring applications and the decreasing cost of manufacturing sensor devices, it is likely that a wide variety of sensor networks will be deployed in the near future. In this environment, the way to access heterogeneous sensor networks and the way to integrate various sensor data are very important. This paper proposes the common system for middleware of sensor networks (COSMOS), which provides integrated data processing over multiple heterogeneous sensor networks based on sensor network abstraction called the sensor network common interface. Specifically, this paper introduces the sensor network common interface which defines a standardized communication protocol and message formats used between the COSMOS and sensor networks.

Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible.

This paper suggests a method to provide the users with better experiences in designing the products with integration of multiple functions and multitasks in aspects of modality. The studies on modality and interruption were carried out from the perspective of cognitive burden on the users when the modality before and after tasks are the same in continuous tasks based on multiple resource theory. Based on this, this study attempted to examine the multitasking support and types of modalities of tasks as well as the interruption orders and frequency when an interruption occurs during a task and how they affect the task performance.

Security and privacy issues are magnified by velocity, volume, and variety of big data, such as large-scale cloud infrastructures, diversity of data sources and formats, streaming nature of data acquisition and high volume inter-cloud migration. Therefore, traditional security mechanisms, which are tailored to securing small scale static (as opposed to streaming) data, are inadequate. In this paper, we proposed Bucket ID Transformation that is a new encryption mechanism and the scheme can range search without order-preserving. Bucket ID Transformation is performed by recursive HMAC as many as a value of Bucket ID. As a future desk, we plan to carry out simulated experiments for performance evaluation and compare the results, and design and verify a provably secure encryption mechanism.

The mobile RFID related technologies that offer similar services as NFC were also analyzed to understand the potential linkage to NFC and the requirements for the linkage. To apply the linking method of two technologies suggested in this paper, the dual tag development with marketability and NDEF decoder development as well as additional technology development, standardization, and patents of related technologies are required. Based on these efforts, if the mobile RFID and NFC technologies are linked, the companies can use the existing infrastructures to reduce the initial technological costs.

Smart grid is a new electricity grid which transmits and distributes electricity intelligently by converging the information technology into the traditional electricity grid. Recently, the smart grid projects are promoted rapidly because the ‘Green IT’ becomes more and more interesting. However, Modernization of the grid will increase the level of personal information detail available as well as the instances of collection, use and disclosure of personal information. This paper’s proposals will contribute the smart grid security and guarantee promotion of the smart grid.

With the advances of wireless sensor network (WSN) technology, various kinds of sensor networks have been developed, and each network has different capabilities. In this paper, we propose a middleware over heterogeneous sensor networks and its query processor which deals with diverse kinds of queries. Since our query processor hides the heterogeneity of underlying sensor networks, applications are able to request identical queries without worrying about the capabilities of target sensor networks. Moreover, we suggest the concept of logical sensor networks and a method to handle them for facilitating the application development. Our experimental results indicate the feasibility and usefulness of our query optimization.

We studied the middleware platform, i.e. COSMOS (Common System for Middleware of Sensor Network), based on multi-agents for uhealthcare services with various types of sensor networks such as the Zigbee wireless sensor network (WSN), the CDMA cellular network, RFID and IPUSN based on the 6LowPAN. Our project is focused on sensor network abstraction for various ubiquitous sensor networks (USN). We defined standard interfaces between USN middleware based on multi-agents and USN networks as well as application services with multi-agents. We implemented several USN services such as u-Healthcare services to consider important issues about middleware platform based on multi-agent applications.

This paper proposes a moving objects data model, query language, main memory database system for location-based service. Location-based services require GIS databases system to represent moving objects and to support querying on the motion properties of objects. For example, fleet management applications may require storage of information about moving vehicles. Also, advanced CRM applications may require to store and query the trajectories of mobile phone users. But, modeling consistent information about the location of continuously moving objects and processing motion-specific queries is challenging problem. We formally define a data model and data types for moving objects that includes complex evolving spatial structure and indeterminacy, and then describe the system implementation.

In this paper, we propose a new data mining technique that can address the temporal relation rules of temporal interval data by using Allen's theory. We present two new algorithms for discovering temporal relationships: one is to preprocess an algorithm for the generalization of temporal interval data and to transform timestamp data into temporal interval data; and the other is to use a temporal relation algorithm for mining temporal relation rules and to discover the rules from temporal interval data. This technique can provide more useful knowledge in comparison with other conventional data mining techniques.