• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.69-78
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• 2022

As the use of Internet of Things (IoT) devices has expanded, digital forensics coverage of the National Police Agency has expanded to smart home areas. Accordingly, most of the existing studies conducted to acquire smart home platform data were mainly conducted to analyze local data of mobile devices and analyze network perspectives. However, meaningful data for evidence analysis is mainly stored on cloud storage on smart home platforms. Therefore, in this paper, we study how to acquire stored in the cloud in a Hey Home Air environment by extracting accessToken of user accounts through a cookie database of browsers such as Microsoft Edge, Google Chrome, Mozilia Firefox, and Opera, which are recorded on a PC when users use the Hey Home app-based "Hey Home Square" service. In this paper, the it was configured with smart temperature and humidity sensors, smart door sensors, and smart motion sensors, and artifacts such as temperature and humidity data by date and place, device list used, and motion detection records were collected. Information such as temperature and humidity at the time of the incident can be seen from the results of the artifact analysis and can be used in the forensic investigation process. In addition, the cloud data acquisition method using OpenAPI proposed in this paper excludes the possibility of modulation during the data collection process and uses the API method, so it follows the principle of integrity and reproducibility, which are the principles of digital forensics.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.309-315
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• 2012

The dissemination of smartphones is being spread and supplementary services using smartphones are increasing and various as the Mobile network and device are developing rapidly, so smartphones that enables to provide a wide range of services is expected to receive the most attention. It makes users listen to music anytime, anywhere in real-time, use useful applications, and access to Internet to search for information. The service environment is changing on PC into Mobile due to the change of the circumstance mentioned above. these services are done by using just location information rather than other context, and users have to search services and use them. It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also defined the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1199-1206
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• 2015

This paper introduces an algorithm for computing similarity between two poses in the motion capture data with different scale of skeleton, different number of joints and different joint names. The proposed algorithm first performs the topological analysis on the skeleton hierarchy for classifying the joints into more meaningful groups. The global joints positions of each joint group then are aggregated into a point cloud. The number of joints and their positions are automatically adjusted in this process. Once we have two point clouds, the algorithm finds an optimal 2D transform matrix that transforms one point cloud to the other as closely as possible. Then, the similarity can be obtained by summing up all distance values between two points clouds after applying the 2D transform matrix. After some experiment, we found that the proposed algorithm is able to compute the similarity between two poses regardless of their scale, joint name and the number of joints.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.174-175
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• 2017

최근 모바일 디바이스 대상 애플리케이션이 급속도로 성장하며 모바일 컴퓨팅과 클라우드 컴퓨팅을 결합한 모바일 클라우드 컴퓨팅의 패러다임이 확산되고 있다. 한편 사물인터넷이 확산되며 다양한 센서 데이터를 수집할 수 있는 사물인터넷 디바이스를 통한 클라우드 컴퓨팅의 필요성이 증가하고 있다. 이러한 IoT 디바이스는 적지 않은 컴퓨팅 성능을 가지고 있음에도 불구하고 대부분 비교적 저수준의 컴퓨팅을 수행하거나 많은 시간을 유휴 리소스의 형태로 낭비하고 있는 실정이다. 본 논문에서는 IoT 디바이스의 리소스를 통합해 모바일 클라우드 컴퓨팅 인프라의 구축 및 운영을 위한 스킴 IDRIS(IoT device's Resource Integration Scheme)를 제안한다. IDRIS는 IoT 디바이스를 통해 수집한 센서 데이터의 처리를 외부 클라우드의 원격 서버가 아닌 사용자가 구성한 개인 모바일 클라우드를 통해 컴퓨팅을 수행할 수 있도록 하는 스킴을 제공한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1110-1116
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• 2015

This paper analyzes global research trend on information security. All technical fields based on information requires security so that discovering technologies (technical terms) which are developing newly or dramatically is able to guide the future direction of the field of information security. In this paper, the ultimate of this research is to figure out the technologies related to information security and to forecast the future through understanding their trends. The paper, as a beginning for the analysis on macroscopic viewpoint, contains measurement of yearly relatedness between technical terms from 2001 to 2014 by using temporal co-occurrence and interpretation of its meaning through comparing the relatedness with trends of top-related technical terms. And to conclude, we could find that Android platform, Big data, Internet of things, Mobile technologies, and Cloud computing are emerging technologies on information security.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.8-15
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• 2012

In this paper, we propose a new monitor for collecting job information from Hadoop clusters in real time. This monitor is made of two programs called Collector and Agent. Agent collects Hadoop cluster's node information and job information, and Collector analyzes the collected information and saves it in a database. Also, Collector was placed in a new node outside the Hadoop cluster so that it does not affect Hadoop's work and will not cause overload. When the proposed monitor was implemented and applied, the testbed cluster was able to detect the occurrence of dead nodes immediately. In addition, we were able to find Hadoop jobs which were inefficient and when we modified such jobs to further enhance the performance of Hadoop.

• Journal of Digital Contents Society
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• v.14 no.3
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• pp.313-321
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• 2013

In recent, proliferation of mobile smart devices have led to big-data era, the importance of location-based services is increasing due to the exponential growth of trajectory related data. In order to process trajectory data, parallel processing platforms such as cloud computing and MapReduce are necessary. Currently, the researches based on MapReduce are on progress, but due to the MapReduce's properties in using batch processing and simple key-value structure, applying MapReduce framework for real time LBS is difficult. Therefore, in this research we propose a suitable system design on efficient indexing and search techniques for real time service based on detailed analysis on the properties of MapReduce.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.313-325
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• 2014

This study seeks to establish a ubiquitous wardrobe with a clothing management function through detailed and subdivided integration research. To create a database, a survey was conducted, and the output data were analyzed and used as the basic data. In particular, this study researched clothing management programs and companies' standards of clothing product classification systems. First, through an in-depth analysis centering on specialists, we established the contents of a ubiquitous wardrobe and used the concept of cloud computing to support the wardrobe contents and smart phone applications. Second, this research found significant differences between individuals, schools, and enterprises in their applications of, and the importance they attach to, design images. A detailed database composed of various categories was established to present the ubiquitous wardrobe contents with efficient functions. Third, we facilitated the search process by designating clothes with QR codes, which is one of the functions of contents. Fourth, the code numbers generated in the process of entering clothes into the database were utilized as RFID information as a way to arrange the products in a simpler manner. The ubiquitous wardrobe was constructed as a web-style hybrid, and its contents areavailable through mobile applications and QR codes.