• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.201-204
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• 2000

In this paper, a wireless engineering field tool based on WCDMA is designed and implemented. Emerging requirements for higher rate data service and better spectrum efficiency are identified for the third generation mobile radio systems. The proposed WCDMA field tool is used for improving the quality of WCDMA service. The current position and time are measured and recorded with CDMA field data. With the system a user can observe the spatial distribution of the field data. For providing concurrency, the system is decomposed of four units and each unit is implemented by using threads.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of Auto-vehicle Safety Association
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• v.4 no.1
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• pp.5-11
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• 2012

This paper discusses the evaluation of the safety impact of the Adaptive Cruise Control (ACC) system in Korea. To evaluate the safety impact, this paper suggests an analysis method by using the test scenario and field operational test data. The test scenario is composed to represent the main component factor of the ACC system and ACC related accident situation such as rear-end collision, lane-change, and road-curvature, etc. Also, from the field operation test data, the system's potential to increase the safety can be measured ideally. Besides, field operational testdata was used to revise the expected safety impact value as Korean road conditions. By using the proposed evaluation method, enhanced safety impact of the ACC system can be estimated scientifically.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2353-2355
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• 2010

Since the efforts in transportation for counteracting Climate Change have been enhanced, it is necessary to reduce GHG emissions from railroad construction. The aim of this study was to develop the calculation method of GHG emissions at the step of railroad construction. Main emission source was the energy consumption from the used heavy equipments. Firstly, GHG inventory including equipments list, energy consumption, and work load was established with the detailed process using standard for the unit cost of construction. Also, the energy consumption of heavy equipments during track construction at A site was collected to compare with the field data. As a result, the GHG emissions between the estimated and the field were a little different, which was caused by the inaccurate field data. Therefore, it is important to manage data efficiently for the calculation of GHG emissions in the field of railroad construction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.36-41
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.777-779
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• 2003

This paper describes the real-time data linkage of the field control system for distributed control in nuclear power plant environment. The most important keys of digital control system in nuclear power plant are the reliability and stability of system, and real-time control ability. This Paper brought up the hardware construction using a new method about the design of each station located upon control transmission network to improve real-time ability of field control system, and measured the station binding time between devices connected to field control module. And it was confirmed performance improvement of overall system for real-time data linkage between control devices.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.2-52.2
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• 2019

Global map of solar surface magnetic field, such as the synoptic map or daily synchronic frame, does not tell us real-time information about the far side of the Sun. A deep-learning technique based on Conditional Generative Adversarial Network (cGAN) is used to generate farside magnetograms from EUVI $304{\AA}$ of STEREO spacecrafts by training SDO spacecraft's data pairs of HMI and AIA $304{\AA}$. Farside(or backside) data of daily synchronic frames are replaced by the Ai-generated magnetograms. The new type of data is used to calculate the Potential Field Source Surface (PFSS) model. We compare the results of the global field with observations as well as those of the conventional method. We will discuss advantage and disadvantage of the new method and future works.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.103-108
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• 2017

In the ubiquitous environment, there has been a problem of transmitting data from various sensors at a long distance. Especially, in the process of integrating data arriving at different locations, data having different property values of data or having some loss in data had to be processed. This paper present a method to analyze such data. The core of this method is to define an objective function suitable for the problem and to develop an algorithm that can optimize this objective function. The objective function is used by modifying the OCS function. MFA (Mean Field Annealing), which was able to process only binary data, is extended to be applicable to fields with continuous values. It is called CMFA and used as an optimization algorithm.

• Journal of Construction Engineering and Project Management
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• v.1 no.1
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• pp.37-43
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• 2011

Labor productivity is a significant factor associated with controlling time, cost, and quality. Many researchers have developed models to define methods of measuring the relationship between productivity and various parameters such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only entails the average productivity data of the construction industry, and it is difficult to predict the time and cost spent on any particular project. As a result, errors occur in estimating duration and cost for individual activities or projects. To address these issues, this research sought to collect data, measure productivity, and develop time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites. It is possible that the result will be used as the EVMS baseline of cost management and schedule management.

• Journal of Soil and Groundwater Environment
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• v.28 no.spc
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• pp.18-39
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• 2023

Rapid development of geophysical exploration and hydrogeologic monitoring techniques has yielded remarkable increase of datasets related to groundwater systems. Increased number of datasets contribute to understanding of general aquifer characteristics such as groundwater yield and flow, but understanding of complex heterogenous aquifers system is still a challenging task. Recently, applications of data science technique have become popular in the fields of geophysical explorations and monitoring, and such attempts are also extended in the groundwater field. This work reviewed current status and advancement in utilization of data science in groundwater field. The application of data science techniques facilitates effective and realistic analyses of aquifer system, and allows accurate prediction of aquifer system change in response to extreme climate events. Due to such benefits, data science techniques have become an effective tool to establish more sustainable groundwater management systems. It is expected that the techniques will further strengthen the theoretical framework in groundwater management to cope with upcoming challenges and limitations.