• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4B
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• pp.316-322
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• 2003

The office data for telecommunication switching systems included system configuration data, processing data, maintenance and administration data on a very large scale. Those data are dependent on functions of the system and the place of system installation. The effect of errors of office data is very serious. In order to reduce time and effort on the system development phase and to enhance system reliability, in this paper we proposed a data analysis and design method for automatically generating office data that are dependent on installation capability and system configuration of the swiching office.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.136-141
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• 2019

In this paper, we describe the design of a small size data acquisition system with STM32 processor based on Cortex-M4. The system is used for the sensor devices to collect raw data on production lines at factory and send them to the server computer in real time. Also the system is designed to easily acquisite various kinds of data collected from various sensors with the digital signal input unit, the analog signal input unit, the digital signal output unit and the analog signal output unit This small data acquisition system will contribute to the improvement of the quality of precision products in the industrial field by collecting various data in real time and transmitting data at high speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.139-144
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• 2023

Recently, many manufacturing companies are paying more attention to energy efficiency due to increased energy costs. Energy-efficient scheduling of production systems is a good method for energy efficiency improvement and cost reduction. In this research, we assumed the tire production problem and aim to construct a production scheduling considering specific shape module types, ordered amount for each tire, number of production modules, and the production time for each type. To facilitate effective production scheduling, we considered the types and number of shape modules currently available, and tire types that can be selected to be produced in the next stage were used as additional inputs, In addition to that, additional production was permitted to reduce the halt of production processing. Thus, an average production module utilization rate of about 62 percent was obtained.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.6
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• pp.739-743
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• 2017

Product distribution and consumption in the military is difficult due to unique contracts and supply systems. It is difficult to change suppliers immediately when quality problem is encountered. Due to these special circumstances, the quality of products must be thoroughly controlled. Sesame oil is used to increase the taste and nutrition of food, but it is more expensive than other cooking oils. Oil producers may blend other cooking oils with sesame oil to make higher profits, so it has become important to identify good and bad products. In this study, pure sesame oil and blend oils were compared by analyzing their smell, taste, chemical components, and near infra-red spectra to determine quality differences between them.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.819-823
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• 2010

The skyline query is one of the effective methods to deal with the large amounts and multi-dimensional data set. By utilizing the concept of 'dominate' the skyline query can pinpoint the target data so that the dominated ones, about 95% of them, can efficiently be excluded as an unnecessary data. Most of the skyline query algorithms, however, have been developed in terms of the numerical data set. This paper pioneers an entirely new domain, the categorical data, on which the corresponding ranking measures for the skyline queries are suggested. In the experiment, the ACM Computing Classification System has been exploited to which our methods are significantly represented with respect to performance thresholds such as the processing time and precision ratio, etc.

• The Journal of Bigdata
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• v.4 no.1
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• pp.83-97
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• 2019

With sensor and communication technology development, spatial data related to business activities is exploding. Spatial data is now evolving into atypical data about space over three dimensions, away from two-dimensional geographic data. In addition to the Fourth Industrial Revolution, which connects the virtual space with the real space, there is a great opportunity for companies to utilize it. The analysis of recent overseas cases shows that it is possible to analyze customized services by understanding the situation of customers and objects located in the space, to manage risk, and furthermore to innovate business processes by analyzing spatial data. In the future, business innovation that combines spatial data from various sources and real-time analysis of relationships and situations between people and objects in space is expected to expand in all business fields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.99-102
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• 2017

대규모의 데이터가 생산되고 저장되는 정보화 시대에서 현재와 과거의 데이터를 바탕으로 미래를 추측하고 방향성을 알아갈 수 있는 빅데이터의 중요성이 강조되고 있다. 정형화 되지 못한 대규모 데이터를 빅데이터 분석 도구인 R과 웹크롤링을 통해 분석하고 그 통계를 기초로 데이터의 정형화와 정보 분석을 하도록 한다. 본 논문에서는 R과 웹크롤링을 이용하여 최근 이슈가 되고 있는 벤처창업을 주 키워드로 하여 뉴스 및 SNS에서 나타나는 벤처창업 관련 빅데이터를 분석한다. 뉴스기사와 페이스북, 트위터에서 벤처창업 관련 데이터를 수집하고 수집된 데이터에서 키워드를 분류하여 효율적인 벤처창업의 방법과 종류, 방향성에 대해 예측한다. 과거의 벤처창업 실패요인을 분석하고 현재의 문제점을 찾아 데이터 분석을 통해 벤처창업의 흐름과 방향성을 제시하여 창업자들이 겪을 수 있는 어려움을 사전에 예측하고 파악함으로써 실질적인 벤처창업에 크게 이바지할 것으로 보여 진다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.354-356
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• 2014

Recently, The sensor technology Has been used in many fields, it is detected data of various types of sensors, is very difficult to integrate due to differences in the standards and each other unit. Also, when we providing a service or program on a data cloud, it is important that integrates of such data in order to take advantage of the detected data in the similar field. In this paper, we propose a approaches to integrating data to be provided as a service in the cloud of data arising from a heterogeneous sensors. The approaches are generating a standard meta-data based on ontology, it is mapping with detected data by the sensor data. Accordingly, the detected data is possible to improve the efficiency of data transfer between the sensor and the application by sending an application in a standard format.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.11
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• pp.403-410
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• 2016

IoT(Internet of Things) has purpose for providing new kind of service though interaction with everything. With development of IoT, computer model which is based on internet is changed to distributed connection model between heterogeneous things. There is a problem that it is impossible to connect between each other different protocols. To solve this problem, we should abstract each of things of data through using adaptor of middleware structure in order to make consistent data unit. In this paper, we propose BLE(Bluetooth Low Energy) adaptor, which is interaction with things, based on DDS(Data Distribution Service) that is real-time standard middleware. It is possible to data interaction between BLE Devices as well as two-way data interaction with different protocol devices. Also existing BLE Devices and study have a problem that Data exchange without using a standard data format of a profile defined by the Bluetooth SIG. Using the data formats defined independently by a problem that should not exchange data according to the type and manufacturer of the device BLE. The BLE adapter to solve this problem, the classification and analysis of the 12 stand profile was applied to create a profile based on the standard data format. It is possible to get wide interoperability of not affected on the BLE devices type and manufacturer of the device because it is applied a profile that standard data format.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.33-40
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• 2022

Recently, IoT-linked services have been used in various environments, and IoT and artificial intelligence technologies are being fused. However, since technologies that process IoT data stably are not fully supported, research is needed for this. In this paper, we propose a processing technique that can optimize IoT data after generating embedded vectors based on machine learning for IoT data. In the proposed technique, for processing efficiency, embedded vectorization is performed based on QR such as index of IoT data, collection location (binary values of X and Y axis coordinates), group index, type, and type. In addition, data generated by various IoT devices are integrated and managed so that load balancing can be performed in the IoT data collection process to asymmetrically link IoT data. The proposed technique processes IoT data to be orthogonalized based on hash so that IoT data can be asymmetrically grouped. In addition, interference between IoT data may be minimized because it is periodically generated and grouped according to IoT data types and characteristics. Future research plans to compare and evaluate proposed techniques in various environments that provide IoT services.