• KIPS Transactions on Computer and Communication Systems
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• v.6 no.1
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• pp.1-8
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• 2017

Communications for mobile sink groups such as rescue teams or platoons bring about a new challenging issue for handling mobility in wireless sensor networks. To do this, many studies have been proposed to support mobile sink groups. When closely looking at mobile sink groups, they can be divided into (multiple) small groups according to the property of applications. For example, a platoon can be divided into multiple squads to carry out its mission in the battle field. However, the previous studies cannot efficiently support the division of mobile sink groups because they do not address three challenging issues engendered by the mobile sink group division. The first issue is to select a leader sink for a new small mobile sink group. The efficient data delivery from a source to small mobile sink groups is the second issue. Last, the third issue is to share data between leader sinks of small mobile sink groups. Thus, this paper proposes a routing protocol to efficiently support the division of mobile sink groups by solving the three challenging issues. For the first issue, the proposed protocol selects a leader sink of a new small mobile sink group which provide a minimum summation of the distance between the new leader sink and the previous leader sink and the distance from the new leader sink to all of its member sinks. For the efficient data delivery from a source to small mobile sink groups in the second issue, the proposed protocol determines the path to minimize the data dissemination distance from source to small mobile sink group by calculating with the location information of both the source and the leader sinks. With regard to the third issue, the proposed protocol exploits member sinks located among leader sinks to provide efficient data sharing among leaders sinks by considering the location information of member sinks. Simulation results verified that the proposed protocol is superior to the previous protocol in terms of the energy consumption.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.48-56
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• 2008

This study provides to car driver with car exhaust gas and sensor information which are car trouble code in engine and many sensors when the car has some problems. This is to provide car manager with many information of car sensors when we go to vehicle maintenance. For example, information of engine RPM, fuel system, intake air temperature, air flow sensors and oxygen sensors can provide to owner or garage, and also add to multimedia system for mp3 files and video files. This system consists of embedded linux system of low power while driving the car which uses OBD-II protocols and zigbee communication interface from CAN communication of car system to self-diagnosis embedded system of car. Finally, low power embedded system has a lot of application and OBD-II protocols for embedded linux system and CAN communication which get sensor informations of car control sensor system while driving the car.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1128-1134
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• 2014

Web based simulation service is actively utilized to computably analyze various kinds of phenomena in real world according to progress of computing technology and spread of Network. However it is hard to share data and information among users on the services, because most of web based simulation services do not share and open simulation processing information and results. In this paper, we design a simulation provenance data sharing service on EDISON_CFD (EDucation-research Integration Simulation On the Net for Computational Fluid Dynamics) to share the calculated simulation performance information. To store and share the simulation processing information, we define the simulation processing step as "Problem ${\rightarrow}$ Plan, Design ${\rightarrow}$ Mesh ${\rightarrow}$ Simulation performance ${\rightarrow}$ Result ${\rightarrow}$ Report." Users can understand a problem solving method through a computer simulation by searching the simulation performance information with Search/Share API of the store. Besides, this opened simulation information can reduce the waste of calculation resource to process same simulation jobs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.6
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• pp.623-629
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• 2002

In transmitting compressed video bit-stream over Internet, packet losses cause error propagations in both spatial and temporal domains, which in turn leads to severe degradation I image quality. In this paper, a new error concealment algorithm, called EBMA(Edge Detection based Boundary Matching Algorithm), is proposed to repair damaged portions of the video frames in the receiver. Conventional BMA(Boundary Matching Algorithm) assumes that the pixels on the boundary of the missing block and its neighboring blocks are very similar, but has no consideration of edges across the boundary. In our approach, the edges are detected across the boundary of the lost or erroneous block. Once the orientation of each edge is found, only the pixel difference along the expected edges across the boundary is measured instead of the calculation of difference along the expected edges across the boundary is measured instead of the calculation of differences between all adjacent pixels on the boundary Therefore, the proposed approach needs very few computations and the experiment shows and improvement of the performance over the conventional BMA in terms of both subjective and objective quality of video sequences.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.127-135
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• 2008

Generally, optimum shortest path algorithms adopt single attribute objective among several attributes such as travel time, travel cost, travel fare and travel distance. On the other hand, multi-objective shortest path algorithms find the shortest paths in consideration with multi-objectives. Up to recently, the most of all researches about multi-objective shortest paths are proceeded only in single transportation mode networks. Although, there are some papers about multi-objective shortest paths with multi-modal transportation networks, they did not consider transfer problems in the optimal solution level. In particular, dynamic programming method was not dealt in multi-objective shortest path problems in multi-modal transportation networks. In this study, we propose a multi-objective shortest path algorithm including dynamic programming in order to find optimal solution in multi-modal transportation networks. That algorithm is based on two-objective node-based label correcting algorithm proposed by Skriver and Andersen in 2000 and transfer can be reflected without network expansion in this paper. In addition, we use non-dominated paths and tree sets as labels in order to improve effectiveness of searching non-dominated paths. We also classifies path finding attributes into transfer and link travel attribute in limited transit networks. Lastly, the calculation process of proposed algorithm is checked by computer programming in a small-scaled multi-modal transportation network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.627-635
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• 2017

Modern warfare is gradually changing into a network war, and information electronic warfare is also progressing. In modern war, electronic warfare is all military activity concerned with electromagnetic field use, such as signal collecting, communication monitoring, information analysis, and electronic attack. The one key function of signal collecting for enemy signal analysis, direction finding, collects the signal radiated from enemy area and then calculates the enemy direction. This paper examined the Watson-Watt algorithm for an amplitude direction finding system and CVDF algorithm for phase direction finding system and analyzed the difference in the direction finding accuracy between in the clean electromagnetic field environment and in the real operating field environment of electronic warfare system. In the real field, the direction finding accuracy was affected by the reflected field from the surrounding obstacles. Therefore, this paper proposesan enhanced direction finding process for reducing the effect. The result of direction finding by applying the proposed process was enhanced above $1.24^{\circ}$ compared to the result for the existing process.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.343-356
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• 2007

Due to advances in position monitoring technologies such as global positioning systems and sensor networks, recent position information of moving objects has the form of streaming data which are updated continuously and rapidly. In this paper we propose an efficient trajectory maintenance method that stores the streaming position data of moving objects in the limited size of storage space and estimates past positions based on the stored data. For this, we first propose a new concept of incremental extraction of position information. The incremental extraction means that, whenever a new position is added into the system, we incrementally re-compute the new version of past position data maintained in the system using the current version of past position data and the newly added position. Next, based on the incremental extraction, we present an overall framework that stores position information and estimates past positions in the stream environment. We then propose two polynomial-based methods, line-based and curve-based methods, as the method of estimating the past positions on the framework. We also propose three incremental extraction methods: equi-width, slope-based, and recent-emphasis extraction methods. Experimental results show that the proposed incremental extraction provides the relatively high accuracy (error rate is less than 3%) even though we maintain only a little portion (only 0.1%) of past position information. In particular, the curve-based incremental extraction provides very low error rate of 1.5% even storing 0.1% of total position data. These results indicate that our incremental extraction methods provide an efficient framework for storing the position information of moving objects and estimating the past positions in the stream environment.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.135-139
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• 2007

Recently Data Centric Storage (DCS) schemes are variously studied for several applications (e.g. natural environment investigation, military application systems and environmental changes monitoring). In DCS scheme, data is stored at nodes within the network by name. There are several drawbacks in the existing schemes. The first is the inefficiency of the range query processing on not considered the locality of store point. the second is the non-homogeneity of store load of each sensors in case of the sensor distribution density is non-uniformed. In this paper, we propose a novel data centric storage scheme with the sensor distribution density which satisfied with the locality of data store location. This scheme divides whole sensor network area using grid and distributes the density bit map witch consist of the sensor density information of each cell. sensors use the density bit map for storing and searching the data. We evaluate our scheme with existing schemes. As a result, we show improved load balancing and more efficient range query processing than existing schemes in environment which sensors are distributed non-uniform.