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In this work, we have implemented the two networked robots that can be reproduced in real time. The two robots formed a network by applying OPC-UA and send location and heading angle information periodically between each robot for lightweight communication environment and object modeling Robot 1 is controlled by the user, and Robot 2 calculates a smoothed traveling path to move from its current position to an updated position and heading angle sent from Robot 1. The results show that the automatically-moving robot’s trajectory are close to the manually controlled robot’s trajectory, and the average communication delay is 20 msec or less.

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 order to improve the safety and efficiency of using material handling equipment for operators, we implemented a two-wheeled robot to assess the autonomous movement technology of the material handling equipment such as forklift, Automated Guided Vehicle(AGV) and crane. The autonomous control system requires a system that identifies the current location and direction of a robot and an operator. We implement the location of each component using the tag recognition technology which utilizes a camera. The operator can send the autonomous movement command to the robot through the Human-Machine-Interface (HMI) device, and the location of the operator and robot can be confirmed through the map information provided by the HMI device. The system collects and uses the location and direction values of the robot and the operator in real time and traces the operator when he/she moves.

This paper describes a system which generates an alert on an abnormal driving situation to a driver, receives a reaction from the driver, and evaluates the effectiveness of the alert. The abnormal driving situations are separated into three types: drowsy driving, reckless driving, and distracted driving. The system consists of several components: sensing component, analysis component, inference component, alert generation component, user interface, and evaluation component. The sensing component measures and collects the data related to the driving situations. The analysis component analyzes the raw data generated by individual sensing component and generates valuable information used to determine the driving situation. The inference component combines those information received from the analysis component and extracts an abnormal driving situation. The alert generation component generates a different alert according to each abnormal driving situation. The user interface displays the alert and receives a reaction from the driver. The evaluation component evaluates the effectiveness of the alert. This system will help prevent vehicle accidents caused by a driver in abnormal driving situations.

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.

In this paper, we offer a new technique to discover frequent spatiotemporal patterns from a moving object database. Though the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and timing constraints on moving sequences makes the computation feasible. The proposed technique includes two algorithms, AllMOP and MaxMOP, to find all frequent patterns and maximal patterns, respectively. In addition, to support the service provider in sending information to a user in a push-driven manner, we propose a rule-based location prediction technique to predict the future location of the user. The idea is to employ the algorithm AllMOP to discover the frequent movement patterns in the user’s historical movements, from which frequent movement rules are generated. These rules are then used to estimate the future location of the user. The performance is assessed with respect to precision and recall. The proposed techniques could be quite efficiently applied in a location-based service (LBS) system in which diverse types of data are integrated to support a variety of LBSs.

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.

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

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.

Implantable devices such as pulse, ECG, and movement sensors that can be embedded in day to day wearables have been drawn a lot of research attentions in the field of wireless sensor network nowadays. In this research, we focus on developing an incremental adaptive network to detect subject at risk of coronary heart disease based on long-term Heart Rate Variability (HRV) measurement under blood pressure and breathing frequency using Poincare plot encoding, named PHIAN. The network is learnt along with the various changes of environment without destroying the old prototype patterns. The error probability density is taken care in the training process, which is necessary to avoid the regions where inputs have a high temporary probability density attracting all neural units. PHIAN is evaluated under different settings and in comparison with previous on-line learning techniques in terms of classification error and the network structure. Our proposed method is efficiently applicable to the smart sensor system to alert the health care service provider to intervene in the emergency situation.

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.

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Mining frequent patterns is the one of them and has been widely studied over the last decade. There are several models and approaches, but there is only one study on the time-sensitive sliding window model. This study spends much memory and has a low accuracy. In this paper, we propose an efficient discounting method and a Sketch data structure for solving these problems. This approach has several advantages. (i) The accuracy is increased compared with that of previous techniques. The efficient discounting method not only loses the information about accumulated count but also decrease many missing true answers. (ii) The memory is saved. The Sketch data structure saves much space. (iii) It is not necessary to have the discount table and reduce significantly the computing time of discounting table. Experiment results show that our proposed method exactly increases the accuracy and saves the memory and the computing time.

Most existing approaches for similarity search did not consider applying calendar concept hierarchy to search for similar patterns from time series data. In this paper, we present two techniques that capture scale oriented features of time series and provide an analyzing method for the multi-resolution view along the time dimension. Especially, we propose that a similarity search which makes the most of calendar concept hierarchy involves three stages which consist of data cube count based on time concept hierarchy, sequence division by time level and feature vector extraction. Because these feature vectors are inserted into multi-dimensional index, pre-processing step executes only one time at the beginning of the search process without adding considerable computing cost. Finally, we show that the proposed techniques find useful knowledge with low computational complexity and discovered rules can be applied to industrial fields.

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.

LBS(Location-Based Service) is generally described as an information service that provides location-based information to its mobile users. Since the conventional studies on data mining do not consider spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the spatial attributes that is changing over time. In this paper, we propose a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions. For this purpose, we use the spatial operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Finally, we show that our technique generates temporal patterns found in frequent moving sequences.