• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.313-318
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• 2016

To utilize sensory data, it is necessary to design architecture for processing and handling data generated from sensors in an IoT environment. Especially in the IoT environment, a thing connects to the Internet and efficiently enables to communicate a device with diverse sensors. But Hadoop and Twister based on MapReduce are good at handling data in a batch processing. It has a limitation for processing stream data from a sensor in a motion. Traditional streaming data processing has been mainly applied a MoM based message queuing system. It has maintainability and scalability problems because a programmer should consider details related with complex messaging flow. In this paper architecture is designed to handle sensory data aggregated The designed software architecture is used to operate an application on the open source framework Storm. The application is conceptually used to transform streaming data which aggregated via sensor gateway by pipe-filter style.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.111-116
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• 2009

In this paper, we propose a phase offset estimation algorithm which is based on turbo coded digital broadcasting system. The phase estimator is an estimator outside turbo code decoder using LMS (Least Mean Square) algorithm to estimate the phase of next state. While the conventional LMS algorithm with a fixed step size is easy implemented, it has weak points that are difficult the channel estimation and tracking in the multipath environment. To resolve this problem, we propose new phase offset estimation method with a variable step size LMS (VS-LMS). Additionally, we propose a scheme which consists of a conventional LMS. The performance is verified by computer simulation according to a fixed phase offset and a increased phase offset, the proposed algorithm improve the bit error rate performance than the conventional algorithm.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.173-180
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• 2011

Various smart-phone applications for location-based service are enabled through mobile communications such as 3G and WiFi. We have developed MARS, an advanced location-based route-search application based on the Android platform. It provides three functions: route-registration, route-search, and route-evaluation. These functions are dynamically maintained by a web server, a database server, and user mobile terminals. As users can update location information using their smart-phone devices, servers provide the information and allow users to add, modify, and remove their own information as well as adding comments to others, while existing map services do not support direct inputs from users. We show our implementation process and demonstration of its operations. We also show the comparison results with existing services. Through these results, we can confirm that MARS can achieve better performance.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1450-1457
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• 2011

Currently most of leak detection monitoring systems use digital maps with paying royalties, and this increases the cost of system construction and financial burdens on local self-governing bodies that manage such systems. Moreover, they have inefficiencies in repair and maintenance, functional expansion, and compatibility with other systems. Thus, this study developed a waterworks pipe network monitoring system that pursues low cost and high efficiency using general-purpose maps on the Internet such as google maps. As this system uses highly compatible free maps, it costs less in construction and its hardware requirements are lower than existing systems, and consequently, overall monitoring performance is enhanced and the cost of construction goes down sharply. This study also proposed a method for pipeline DB construction, which can be started together with the construction of the monitoring system, in order to improve the field applicability of the system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.101-106
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• 2014

Information and Communication Technology environment has been developing rapidly and variety of services are in service. As data becomes increasingly sophisticated. These data was applied techniques of visualization in order to visualize efficiently. Various agencies are providing the map based data in real-time. However, traffic information is getting more complex and users are having a difficulty to understand with convential visualization techniques. This study was designed and implemented in the web service of traffic information using Focus+Context. Web service implemented HTML5(Hyper Text Markup Language 5), and it runs on browser of either desktop or mobile devices. This study sets an example as web application from a user perspective by combining information visualization and traffic information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.109-116
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation for the identification of a face shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the face area through pre-processing using a face shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a face area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the face shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.191-199
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• 2020

Cyber warfare training is a key factor for boosting cyber warfare competence. In general, cyber warfare training is conducted by scenarios, and the effects of training can be enhanced by including various elements in the scenarios that can improve the quality of training. In this paper, we introduce the training information, network map, traffic generation policy, threat/defense behavior identified as elements to be included in training scenarios, and propose a method of authoring training scenarios by layering and combining them. We also propose a database design for integrated management of each scenario layer. The layered training scenario authoring method has the advantage of increasing convenience of authoring by reusing existing layers and extending training scenarios based on various combinations between the layers.