Hybrid system is a dynamic system that is composed of both a continuous and discrete system, suitable for automobile, avionic and defense systems. Various modeling languages and their supporting tools have been proposed and used in the hybrid system. The languages and tools have specific characteristics for their purpose. Electronics and Telecommunications Research Institute (ETRI) proposed a hybrid system modeling language, ECML (ETRI CPS Modeling Language). ECML extends DEV&DESS (Differential Event and Differential Equation Specified System) formalism with consideration of CPS (Cyber-Physical System), which supports modeling and simulation. In this paper, we introduce ECML and suggest a formal definition. The case study specifies a simple vehicle model using the suggested formal definition.

Recently, wellness has become an issue related to improvements in personal health and quality of life. Data that are accumulated daily, such as meals and momentum records, in addition to body measurement information such as body weight, BMI and blood pressure have been used to analyze the personal health data of an individual. Therefore, it has become possible to prevent potential disease and to analyze dietary or exercise patterns. In terms of food and nutrition, analyses are performed to evaluate the health status of an individual using dietary data. However, it is very difficult to process the large amount of dietary data. An analysis of dietary data includes four steps, and each step contains a series of iterative tasks that are executed over a long time. This paper proposes a problem solving environment that automates dietary data analysis, and the proposed framework increases the speed with which an experiment can be conducted.

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

Seas and swells are basic wave types in ocean surface simulation and are very important elements in the simulation of ocean background. In this paper, we propose a real-time simulation method, for reproducing realistic seas and swells, to be used in real-time simulators such as ship maneuvering simulators. Seas and swells have different visual properties. Swells have relatively longer wavelengths and round crests compared with seas, therefore they are visualized globally with large meshes and procedural methods. Parameters to illustrate swells are extracted from ocean wave spectra. Conversely, seas have shorter wavelengths and their characteristics are only clearly apparent near to the observation point. Here, we present visualization of seas based on a statistical wave model using ocean wave spectra, which provides realistic results in a reactively small area.

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

In the defense field, weapon systems are increasing in importance, as well as the weight of the weapon system embedded software development as an advanced technology. As the development of a network-centric warfare has become important to secure the reliability and quality of embedded software in modern weapons systems in battlefield situations. Also, embedded software problems are transferred to the production stage in the development phase and the problem gives rise to an enormous loss at the national level. Furthermore, development companies have not systematically constructed a software reliability test. This study suggests that approaches about a qualityverification- system establishment of embedded software, based on a variety of source code reliability test verification case analysis.

The barcode area extraction from images has been extensively studied, and existing methods exploit frequency characteristics or depend on the Hough transform (HT). However, the slantedness of the images and noise affects the performance of these approaches. Moreover, it is difficult to deal with the case where an image contains multiple barcodes. We therefore propose a barcode detection algorithm that is robust under such unfavorable conditions. The pre-processing step implements a probabilistic Hough transform to determine the areas that contain barcodes with a high probability, regardless of the slantedness, noise, and the number of instances. Then, a frequency component analysis extracts the barcodes. We successfully implemented the proposed system and performed a series of barcode extraction tests.

Software project planning includes several processes for estimating the effort required to complete software project tasks, allocating human resources to tasks, and creating a project plan. Because software planning is becoming more complicated as the size of software projects grow, it is difficult for project managers to decide on a reasonable project plan. To help them, many automatic software project planning approaches have been proposed. The approaches all focus on minimizing project duration. But if the plan is simply to minimize the duration, without considering software quality, the plan can harm the eventual software quality. In our research to create a reasonable project plan, we consider software quality as well as duration of the project, by defining a software quality score. The project manager can plan the project to maximize software quality for a specific project duration.

In order to solve problems happening in the practical environment of complicated system, the importance of the self-adaptive system has recently begun to emerge. However, since the differences between the model built at the time of system design and the practical environment can lead the system into unpredictable situations, the study into methods of dealing with it is also emerging as an important issue. In this paper, we propose a method for deciding on the adaptation time in an uncertain environment, and reflecting the real-time environment in the system's model. The proposed method calculates the Bayesian Surprise for the suitable adaptation time by comparing previous and current states, and then reflects the result following the performed policy in the design model to help in deciding the proper policy for the actual environment. The suggested method is applied to a navigation system to confirm its effectiveness.

In this paper, to build a parallel corpus between Korean and English in Wikipedia. We proposed a method to find similar sentences based on language resources and topic modeling. We first applied language resources(Wiki-dictionary, numbers, and online dictionary in Daum) to match word sequentially. We construct the Wiki-dictionary using titles in Wikipedia. In order to take advantages of the Wikipedia, we used translation probability in the Wiki-dictionary for word matching. In addition, we improved the accuracy of sentence similarity measuring method by using word distribution based on topic modeling. In the experiment, a previous study showed 48.4% of F1-score with only language resources based on linear combination and 51.6% with the topic modeling considering entire word distributions additionally. However, our proposed methods with sequential matching added translation probability to language resources and achieved 9.9% (58.3%) better result than the previous study. When using the proposed sequential matching method of language resources and topic modeling after considering important word distributions, the proposed system achieved 7.5%(59.1%) better than the previous study.

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.

In recent years, many research efforts have been made on privacy-preserving data mining (PPDM) in data of large volume. In this paper, we propose a PPDM solution based on principal component analysis (PCA), which can be widely used in computing correlation among sensitive data sets. The general method of computing PCA is to collect all the data spread in multiple nodes into a single node before starting the PCA computation; however, this approach discloses sensitive data of individual nodes, involves a large amount of computation, and incurs large communication overheads. To solve the problem, in this paper, we present an efficient method that securely computes PCA without the need to collect all the data. The proposed method shares only limited information among individual nodes, but obtains the same result as that of the original PCA. In addition, we present a dimensionality reduction technique for the proposed method and use it to improve the performance of secure similar document detection. Finally, through various experiments, we show that the proposed method effectively and efficiently works in a large amount of multi-dimensional data.

In opportunistic networks, each message is delivered to the destination by repeating, storing, carrying, and forwarding the message. Recently, with the vitalization of social networks, a large number of existing articles have shown performance improvement when delivering the message and considering its social relational networks. However, these works only deliver messages when they find nodes, assuming that every node cooperates with each other unconditionally. Moreover, they only consider the number of short-term contacts and local social relations, but have not considered each node's average relation with the destination node. In this paper, we propose novel message sending techniques for opportunistic networks using nodes' social network characteristics. In this scheme, each message is delivered to the destination node with fewer copies by delivering it mostly through nodes that have high intimacy with the destination node. We are showing that our proposed scheme presents a 20% performance increase compared to existing schemes.

From 2009, the market of smart devices has been rapidly increasing. These devices provide various services to users. The cloud messaging service, especially, is applied to many various services, and sends messages asynchronously. In the cloud messaging service, there are two methods for message transmission, message transmission based on an IP address and a publish/subscribe technique. Each technique uses basic messages in order to send messages to mobile devices. In this paper, the hybrid message transmission, based on user-customized analysis to reduce basic messages, is proposed. The hybrid message transmission uses Exponential Moving Average (EMA) and K-means algorithms for user-customized analysis, and determines the message transmission techniques in each timeslot.