• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.21-30
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• 2017

IoT(Internet of Things) can enable networking and computing using any devices is rapidly proliferated. In the existing IoT environment, bottlenecks and service delays can occur because it processes data and provides services to users using central processing based on Cloud. For this reason, Edge Computing processes data directly in IoT nodes and networks to provide the services to the users has attracted attention. Also, numerous researchers have been attracted to intelligent service efficiently based on Edge Computing. In this paper, expert system-based context awareness scheme for Edge Computing in IoT environment is proposed. The proposed scheme can provide customized services to the users using context awareness and process data in real-time using the expert system based on efficient cooperations of resource limited IoT nodes. The context awareness services can be modified by the users according to the usage purpose. The three service modes in the security system based on smart home are used to test the proposed scheme and the stability of the proposed scheme is proven by a comparison of the resource consumptions of the servers between the proposed scheme and the PC-based expert system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.1-10
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• 2011

Recently, cloud computing which has provided enabling convenience that users can connect from anywhere and user friendly environment that offers on-demand network access to a shared pool of configurable computing resources such as smart-phones, net-books and PDA etc, is to be watched as a service that leads the digital revolution. Now, when business practices between departments being integrated through a cooperating system such as cloud computing, data streaming between departments is getting enormous and then it is inevitably necessary to find the solution that person in charge and find data they need. In previous studies the clustering simplifies the search process, but in this paper, it applies Hash Function to remove the de-duplicates in large amount of data in business firms. Also, it applies Bayesian Network of data mining for classifying the respect data and presents handling cloud computing based data. This system features improved search performance as well as the results Compared with conventional methods and CPU, Network Bandwidth Usage in such an efficient system performance is achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.79-90
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• 2016

Autonomous knowledge service, a fully-automated and pervasive service for knowledge acquisition and support based on the power of recent ITs is gaining tremendous interest more and more, as not only the level of users' intelligence increases but also the maturity of IT infrastructure improves. Conventional approaches of knowledge service, however, could not satisfy users because they usually provided undesired knowledge which had been acquired without considering users' want. In other words, knowledge acquisition and distribution were separately performed. This research, therefore, suggests an amended autonomous knowledge service framework by fully-automating the whole phases of knowledge life cycle, from knowledge acquisition to distribution. ASKs, the prototype system of this research, is also implemented by defining and specifying component technologies which constituently compose suggested framework. More user-friendly and applicable way of knowledge service will be derived and facilitated through this research.

• Spatial Information Research
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• v.19 no.4
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• pp.91-97
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• 2011

In this paper, we propose an architecture for geosemantic services. With the rapid progress of web services, wireless internet technologies and popularization of smart phone in recent years, a lot of applications based on geographic information are being developed. Moreover the search portals empowered by semantic web technologies are enabling general users to access on-line resources more easily. However, several studies in GIS domain have pointed out the practical limitation of existing service patterns, which are limited only to linking heterogenous spatial databases, insufficient for several important use cases. Hence we draw functional elements of geosemantic services from GIS and semantic web standards, and present the use cases and a new architecture for geosemantic services. This approach could set a foundation to implement geoemantic services.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.789-799
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• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.137-145
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• 2016

The explosive growth of social networking services based on smart devices popularize these relationships and activities online in accordance with the far larger impact of this on the real life offline, the interest and importance for the online activity is increasing. In this study, factors affecting the SNS activity are defined by object, user, influence direction, influence distance and proposed a method to measure organic terms in effect between the SNS users. Influence Direction and Influence Strength (or Distance) are elaborated by using the existing influence measurement element such as structured data - the number of friends, the difference between the number of contacts - and the new influence measurement element such as unstructured data - gap between the former time and the latter time, preference and type of response behavior - that occur in social network service. In addition, the system for collecting and analysing data for measuring influence from social network service and the process model on the method for measuring influence is tested by using sample data on Facebook and explained the implementation probability.

• The Journal of Bigdata
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• v.1 no.2
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• pp.9-19
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• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1089-1096
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• 2019

Recently, small-scale IoT services using a small amount of information through low-performance computing have been spread. It requires low cost, low-power, and long-distance communication technologies with wide communication radius, relatively low power consumption. This paper proposes a MAC layer and routing protocol that supports multi-hop transmission in small-scale IoT environment distributed over a large area based on LoRa communication and delivering a small amount of sensing data. The terminal node is mobile and the communication type provides bidirectional transmission between the terminal node and the network application server. By applying the proposed protocol, a production line monitoring system for smart factory was implemented. It was confirmed that the basic monitoring functions are normally performed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1307-1312
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• 2015

As smart-phones become popular, mobile data traffic has been dramatically increasing and intensive researches on the next-generation mobile communication network is in progress to meet the increasing demand for mobile data traffic. In particular, heterogeneous network (HetNet) is attracting much interest because it can significantly enhance the network capacity by increasing the spatial reuse with macro and small cells. In the HetNet, we have several problems such as load imbalance and interference because of the difference in transmit power between macro and small cells and cell range expansion (CRE) can mitigate the problems. In this paper, we propose a new cell selection scheme with adaptive cell range expansion bias (CREB) for ultra dense HetNet and we analyze the performance of the proposed scheme in terms of average cell transmission rate through system-level simulations and compare it with those of other schemes.