Earticle

Museums have been evolved for a long period of time to tell stories. It was a place of accommodating exhibitions the time required, and changing patterns in them have made identity of museums in diverse forms through spaces and media. Among them, literatures house is a museums that exhibit literatures, which are the oldest form of medium. Besides, it has the responsibility to convey the symbolism in terms of content and the intention, too, clearly. As such, it has been at a special position that people who created the work should be included in the exhibition as well as the work that exist as the story. Finally, the literatures house is a place that exhibit typed information exposed to public already in terms of material, so that it is difficult to plan the exhibition in order to go beyond viewers’ expectations. The characteristics of literature should be considered as a media, and firstly, it should be approached as a literary genre to analyze storytelling of itself, and how to transform it into the space should be researched together. In this research, fairy tales which have the strongest attraction motivation were selected among the literature genre in consideration of future viewers of literature museums, and their storytelling characteristics were studied, and then how to apply them into exhibition planning was contemplated. As a result, the direction of storytelling was studied for exhibition at literature museums.

This paper designed and implemented a next-generation solution for the Internet Broadcasting system applying FlashVideo. Compressing HD Class images in real time currently being broadcast in the field, this is a system that can service VOD as well as Internet live broadcasting, which is a solution that supports the operation of Internet Live broadcasting, VOD service and UCC service easily online. It is a system that supports real time streaming of MPEC4 and WMV images on the Internet or Smartphone by compressing camera and VOD images using H264 Codec in real time, which is first, the first real time encoder system in Korea with the real time recording feature of camera images and a software product that supports the up-to-date Codec technology suitable for the Web and Smartphone environments. Second, it is a two-way Internet broadcasting system that can play videos with an MP4 player and support user chatting and customizing. Third, the Contents Management System (CMS) feature can service streaming of video contents and lecture management contents in real time through Android phone or iPhone.

In this paper, focus is on energy aware model of wireless sensor networks in which each sensor node randomly and alternatively stays in an active or sleep mode. The active mode consists of two phases, the full-active phase and the semi-active phase. When a particular sensor node is in the full-active phase of the active mode, it may sense data packets, transmit the sensed packets, receive packets, and relay the received packets. However, when the phase of the sensor node switches from the full active phase to the semi-active phase, it is only able to transmit/relay data. When the particular sensor node is in a sleep mode, it does not interact with any other node. In this work, emphasis is also given on numerical analysis for energy aware model. An energy aware model for WSN is being also developed with active and sleep features and justified with mathematical analysis and results thereon.

Sender-based message logging can considerably lower high failure-free overhead of receiver-based message logging resulting from synchronously logging each message into stable storage by using volatile memory of its sender as storage for logging. This beneficial feature can be obtained at the expense of extra communication costs required for allowing message senders to get receive sequence numbers of the messages from their receivers and confirm them with the receivers and slowness and complexity of recovery of each failed process coming from its obtaining message log from the corresponding senders. However, attempting to apply sender-based message logging into large-scale ubiquitous sensor networks requires reducing the number of messages passing on core networks during its message logging and recovery procedures. This paper presents a scalable virtual sender-based message logging algorithm to solve this problem by enabling the broker elected in a group of nodes as virtual sender to localize both of the logging and recovery procedures to a maximum. We show how this algorithm can guarantee the system consistency in case of sequential failures and perform better than the conventional one in terms of message overhead.

The social networks are based on sensor networks by mobile phones where social applications, such as real-time collaborative video watching applications, work out well, sensing their events collaboratively and socially. Recently, these networks use a content-based distributed P/S (publish/subscribe) infrastructure that acts as a scalable group communication backbone. P/S based gossip protocols is elastically to scale in and out and provides suitable consistency guarantees for data safety and high availability but, does not deal with end-to-end message delay and message order-based consistency, which are addressed in real-time collaborative applications. Especially in broadcasting in real-time collaboration applications, it is possible for the messages to take different time and arrive at end users in different order. So, these applications should be based on P/S infrastructure including dealing with message timeliness and message ordering consistencies. Gossip communication is becoming one of the promising solutions for addressing P/S scalability problems in providing information propagation functionality by exploiting a mixture of diverse consistency options. In this paper, we present deadline-constraints causal order protocol respecting Δ(lifetime) based on P/S architecture for broadcasting in real-time collaborative applications in social networks to guarantee causally ordered messages delivery from brokers to subscribers. In the proposed protocol, every broker manages a 2-dimensional vector, representing its knowledge of the last message sent by each broker at time t. But, every broker disseminates a broadcast message only with one scalar variable, the time-stamped information that represents the maximum gossip round and is the deadline (lifetime) of the immediate message of it, to subscribers because all messages disseminated by brokers have the same lifetime as the maximum number of gossip rounds. Therefore, the proposed protocol implemented for P/S based on gossip protocols results in very low communication overhead from brokers to subscribers in the context of broadcast respecting Δ.

Through analysis of mechanism and characteristics of Session initiation protocol (short for SIP), it is found that SIP possesses prominent flexibility and expansibility, which meet the requirement of Internet infrastructure and media flow transmission. To some extent, tight-coupling model based on SIP reflects the fundamentals of distance education and session initiation. Furthermore, while key control and media mixer are functionally separated. A Web server is added to improve mutual communication. The model SIP-based distance education system with three centralized servers are constructed and the corresponding topology structure is proposed. The mechanism and education function of the model are embodied and realized through several aspects, such as distance class creation, participation and termination, state information notification and education resource control.

Since its introduction in the late 90’s, Augmented Reality (AR) has emerged as a killer service in various domains. In contrast to conventional monolithic AR services that do not consider individual user contexts, AR services are currently evolving into their next phase. Two important requirements for next generation AR services include a personalized context-aware presentation of AR information and the provisioning of an enhanced user experience in a more realistic manner. Therefore, in this paper, we present methods for supporting such functionalities using the Binary Format for Scenes (BIFS) technology standardized by the Moving Picture Experts Group (MPEG).

In this research we implement an EEG based music therapy. Music therapy can help the student deal with the stress, anxiety and depression problems. To do so we will develop EEG-based human emotion recognition algorithm. Proposed training program works as a therapist. The music choice and duration of the music is adjusted based on the student’s current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

Multimedia in the network has been fully developed. It used in education also has a long history and resources in education network system become very rich. Now, number of multimedia files in school server is still increasing now. How terminal computers in each place in school get multimedia files at fast speed with low energy consumption is a problem worthy to solve. In order to solve the problem, a new method of dynamic algorithm based on PSO (Particle Swarm Optimization) and ACO (ant colony optimization) method has been developed. This method is based on peer to peer mechanism and can be used in school with different network speed area. In the algorithm, the whole network is firstly divided into several subsystems. Each subsystem would be composed of computers with different network speed. One terminal computer would get multimedia files through computers with high network speed. This method can get high speed in fetching multimedia files and decrease energy consumption. The method would be firstly used in the education system then popularize into other systems.

In this paper, a hidden Markov model (HMM) based distributed text-to-speech (TTS) system is proposed to synthesize the voices of various speakers in a client-server framework. The proposed system is based on speaker-adaptive training for constructing HMMs corresponding to a target speaker, and its computational complexity is balanced by distributing the processing modules of the TTS system at both the client and server to achieve a real-time operation. In other words, fewer complex operations, such as text inputs and HMM-based speech synthesis, are conducted by the client, while speaker-adaptive training, which is a very complex operation, is assigned to the server. It is shown from performance evaluation that the proposed TTS system operates in real time and provides good synthesized speech quality in terms of intelligibility and similarity.

In this paper, we present realistic farming trainer for experiencing crop cultivation and livestock breeding simulation. Farming simulation utilizing game experience has been presented by various formats but current researches have not presented balanced farming experience and amusement to users. We achieved both education purpose and entertainment feature by realistic farming trainer using historic simulation and gaming elements. To achieve realistic educational purpose, we implemented realistic farming simulation using historic simulation, which covers last ten years of weather, disease, and other factors. Also, we use storyline with quest design to give amusement to users. Technical details include (1) Crop simulator, (2) Livestock simulator, (3) Weather simulator, (4) Disease and insect simulator, (5) Fertilizer simulator and (6) Business simulator. Experimenting with sample user groups, they can easily learn how to farming and livestock management with amusements.

Community detection is an important issue in social network analysis, which aims at finding potential community structures such that the internal nodes of a community have higher closeness than external nodes. Taking into account node attribute information, this paper presents an improved community detection algorithm based on random walk. Based on the basic understanding that people getting together often relies on their common interests, node similarities are initially calculated with node attributes and iteratively updated based on the random walk model. Meanwhile, node importance is computed to represent how much it can influence other nodes, based on which some important nodes are selected as seeds for community clustering. As for overlapping community detection, some construction is made on a given social network. Experimental results on several real datasets show our approach has better effects than previous methods on both overlapping and non- overlapping communities.

In this paper we measured individual emotions and sense of presence. This is measured by two methods, a questionnaire survey and electroencephalography (EEG), in order to obtain objective data. EEG brain signals were measured when participants looked at the moving images. The psychophysiology test results of questionnaire survey and EEG for the figurative images were good. In this paper we revealed that the concept of presence is a psychological and physiological reaction related with the individual emotions to the images and that EEG signals can be used as one of the important tool for the visual arts.

We present a methodology about BCI (brain computer interface) based environment system for immersion of FPS game play. FPS (First- person shooter) game is a video game genre centered on gun and projectile weapon-based combat through a first-person perspective, immersion is important factor in this game. FPS game designer effort to raise players immersion when make game using interface, graphic effect, sound etc. We implemented real-time game environment changing system using the BCI to raise the degree of immersion in FPS game. EEG1 is the recording of electrical activity along the scalp. We measure user’s EEG using MindSet2 for implemented game playing time to get user’s concentration rate. This value operates our environment system in virtual space. In virtual space, each player experience different environment situation because each player has various concentration rate. Implemented environment system is formed with alternation of day with night and weather. Human body condition is changed depending on day and night. Player can feel change of character’s condition via player’s condition by our system. Implemented system can be applied to various game genres to raise player’s immersion for game playing time.

Surface reconstruction from gradient fields is of wide application in computer vision fields. Traditional methods usually enforce surface integrability in discrete domain, while current kernel approach suffers the problems of parameter choice. In this paper, we propose a novel method, i.e. kernel gradient regression, to reliably reconstruct surfaces. The box-spline kernel, instead of the common Gaussian kernel, is deployed in surface reconstruction due to its compact support and parameter robustness. To our knowledge, this is the first time to prove the special box-spline function as a new kind of positive definite spline kernel. The target surface is recovered under least-squares sense from the gradient fields, by converting the reconstruction problem to its kernel representation. Experimental results show that our proposed method outperform available approaches in preserving sharp edges and fine details, without prior knowledge of depth discontinuity.

The conventional researches on a distributed On-Line Analytical Processing (OLAP) system have been in hardship to be adapted to real business environment. However, the recent spread of Cloud PaaS (Platform as a Services) provides new chances in the field of a distributed OLAP. OLAP query execution costs many minutes by its enormous data and OLAP query properties. On the other hand, MOLAP has fast responses. But it has a physical space limit to materialize all cells in possible combinations. Therefore, MOLAP is unsuited to analyze large data. In this paper, to provide not only flexibility and expandability of ROLAP but also the speediness of MOLAP, the cloud server architecture is proposed which shares clients’ cube cache by P2P and manages central index on cube data on P2P nodes. In this paper, each node acts as not only a P2P duplicator of aggregated data cubes but also a hybrid server to process queries on sub-cubes. Also, the requests on server are confined to non-aggregated areas and multiple client nodes exchange data simultaneously and asynchronously in the proposed Cloud P2P OLAP. In particular, data are retrieved from physically or logically adjacent nodes. With time series properties, the volume of requested data is minimized and the reuse of past cache data is focused. While central managed P2P has blocked the extendibility, Cloud P2P OLAP guarantees the performance and the extendibility. While Grid OLAP should keep self-distributed system, Cloud use services as much as used by public services. Therefore, Cloud P2P OLAP solves a lot of theoretical limits of conventional distributed OLAP.

In this paper, we propose a method which implements Shannon’s information theory on entropy evaluation and use the results for weight decision. We assume that there are two conventional methods, LA and ELA. Using Shannon’s information theory we obtained entropy values which are used as weights for choosing interpolation method. The original image is downsampled to be low resolution image, where we apply Shannon’s entropy evaluation equation. Finally, result image is obtained by weighted interpolation between LA and ELA. Simulation results show the proposed method gives satisfactory results.

To solve the problem of three-dimensional terrain simplification in large-scale which was based on digital elevation model (DEM) data, a terrain simplified method based on the theory of spatial autocorrelation was proposed. The slope, a key factor for expressing the terrain features, has been taken into account in this method. According to principle of regional similarities in geomorphology is related to DEM data correlation, a cluster analysis was performed on the terrain data to get the thresholds distinguish terrain features. On the basis, the gradient-based distance-weighted method was adopted to fit elevation values of the center point and generated a new terrain mesh. The results of experiments show that the method to some extent reduces the size of the data, while maintaining good terrain features and curvature characteristic.

This paper proposes a finger actions sensing-based robot motion authoring system. Based on finger actions sensing and their recognition, the proposed robot motion authoring system allows users easily to create and control robot motion according to the number and events of fingers. Furthermore, the system can be used to simulate user-created robot contents in the 3D virtual environment. This allows the users to not only view the authoring process in real time but also transmit the final authored contents. The effectiveness of the proposed motion authoring system was verified based on various motion authoring simulations of an industrial robot

Estimation of walking direction is important for providing detailed information about an individual’s gait. This study proposes a method for estimating walking direction using a shoe-mounted acceleration sensor. Heel contact is determined from the negative peak of the acceleration magnitude, and foot inclination is calculated for estimating the horizontal acceleration. The stepping direction of each foot is calculated through principle component analysis along the horizontal acceleration. The walking direction is estimated by averaging consecutive left- and right-step directions. The proposed method was applied to out-door straight ground walking for 658 steps, and yielded a mean difference of 0.48° with a standard deviation of 1.98°.

Based on the library questionnaires of Hebei Vocational & Technical College of Building Materials in 2013, this paper analyzes the status and causes of electronic resources utilization. After the analysis, this paper puts forward the corresponding countermeasure on how to improve the utilization of electronic resources in vocational colleges in an attempt to push on the promotion and utilization of electronic resources.

Increasing amounts of TV contents are being disseminated through advanced broadcast, satellite and smart TV technologies leading to an information overload. Despite the excessive increase of TV contents, there are few considerations in the development of novel methods for personalized smart TV content recommender services. Most existing personalized TV content services are mainly focus on using individual profiles and require user’s immediate participation in rating their experienced contents. In this paper, we propose the context tagging-based user’s preference prediction mechanism by extending the widely known recommender algorithm, collaborative filtering (CF) in order to increase the user’s satisfaction about the recommender service. The development of the prototype system shows the usefulness of proposed mechanism. And the experiment confirms that the proposed mechanisms improve the recommender system.

BitTorrent is an issue for copyright protection and infringements should be punished based on evidence of invasion of the rights of others. In the BitTorrent system, seeders possess the complete content and leechers have partial content, and the content is shared among peers. Copyrighted content is easily distinguished with complete content but it might not be obvious with partial content. Thus, we propose an original content recovery technology based on Pulse Code Modulation pieces obtained from the CD ripping of music held by leechers. The proposed technique uses a correlation-based method that exploits the similarity of music. We verified that the proposed algorithm could recover pieces of files by comparing the output value of a correlator with that of the input piece, using a number of wave file channels and 8/16 bits.

About the abnormal behavior of business process model, it is the core of work of business process management to determinate the change region which causes abnormal situation. The existing methods of researching change region are based on comparison with source model (or reference model), while it’s difficult to obtain source model actually, so these methods are lack of practical operability. In this paper, we construct object sub-model of business process model with labels based on the analysis of behavioral profile of Petri net. A method is proposed to determine change region and the smallest change region based on the concept of action pattern. We evaluate the novel method using a business process model in e-commerce.

In our previous work, a user similarity-based contents recommendation service using NFC was proposed for the same goal. This service used a small sample because it used only information of the user who had watched a museum. However, it has been shown that there are some limitations resulting from the difficulty of accurately predicting the user's preference. In order to lift this drawback, this paper introduces a user taste prediction service using big data for improving user-friendliness to a maximum. The proposed service predicts the user's taste using big data such as Twitter and blogs. It is possible to predict the exact user's preference and might recommend more suitable contents to the user's taste because it predicts the user`s taste using big data with a variety of user's social network information. So, it can recommend contents that match the user's taste. Our simulation results show the proposed big data-based approach can give each museum visiting user more accurate recommendation service appropriate to his or her taste compared with the previous one in terms of user preferences to exhibition-related contents.

Electronic calendar has been intensively used in people’s life and work. In recent years it is popularized in the context of social networks, which forced the emergence of social calendar. For social calendar people is not just satisfied with creating, storing and exchanging their schedule data, they wants to use the calendar as a social network app, send invitations on some scheduled events, flexibly define the access rules on their scheduling, and even create event agenda in team manner. This paper describes a framework to answer those requirements. Using the standard interoperaterable calendar data format - iCalendar as calendar representation, the framework compiles the event information into an internal network structure, and incorporates a MapReduce consistency algorithm - that makes the framework suitable for large scale data processing in social network context.

Wireless mesh networks (WMNs) have been proposed for realizing their goals, such as throughput and full connectivity, in wireless networks. The interference between nodes in WMNs is a critical limiting factor. Understanding of interference needs to develop novel approaches, such as channel re-assignment, route selection, and fair scheduling. Actually, throughput of WMNs has degraded when reassigning channels due to the intra and inters interference. In this paper, we study the extent problem of interference, which spreads the trials of channel re-assignment toward neighbor mesh routers. In particular, the extent of interference causes additional throughput degradation when compared to a pair-wise interference to a small fraction of the links. This implies that the pair-wise interference measurements may be optimistic when used to drive protocols in wireless mesh networks. We newly define an analytical model, which is referred to as a ripple effect problem. The defined model is a formal model of interference to estimate the maximum rate at which flows can safely send traffic without overloading network. The proposed model can exemplify how protocols should take the extent of interference into account. The simulation results show that the proposed model can exactly evaluate the attempts of channel re-assignment occurred from mesh networks.

Recently, the prevalence of high-performance mobile devices and development of IT technologies, including augmented reality technology and location information service, etc. has led to the formation of an educational environment allowing learners to practice context learning theory ideally, subsequently contributing to active progress of relevant studies. Therefore, it is difficult to expect leaners to have an interest and a commitment since it cannot connect a lot of information coming through the perspective of the learner in a real-world environment with situated learning in real time. Accordingly, this paper proposes a intelligent English situated learning system that can practice situation context learning more reasonably using a location-based service and a recognition technology that automatically recognizes text information on street signboards that are easily accessible in everyday life, yet provide learners with a lot of information. The proposed system provides learners with English conversation learning contents that can be used in the business sector related to trade name recognized through text information on street signboards from images captured by cameras.

A policy is a set of ideas or plans that is used as a basis for making decisions, especially in politics, economics, or business, and it serves as a reference for the organization’s activities or specific individual. In this sense, it is important to strengthen the capability of security performance. When it comes to Industrial Control Systems (ICS) which considerably affect the national security, policy compliance with consideration of ICS’s characteristic is crucial. This paper is based on data from evaluating the degree of compliance of specific country’s infrastructure with survey. Method used in this paper consists of four steps. First, an employee of specific company participates as a subject and evaluates the compliance of his/her organization with personal discretion. Second, determine criteria for further analysis. Third, analyze data for existence and compliance of policy. Fourth, calculate ‘Policy-Domain-System (P-D-S)’ index from data processed in the previous step. PDS and ISMS data can be used at the same time. This paper will contribute the efficiency of existing ISMS standard.