• Geophysics and Geophysical Exploration
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• v.14 no.3
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• pp.203-213
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• 2011

Time-lapse seismic surveys make repeated seismic surveys at different stages of oil production of a hydrocarbon reservoir to monitor changes in reservoir like fluid saturation. Since the repeatable surface seismic measurements can identify fluid types and map fluid saturations, oil and gas companies can make much more informed decision during not only production but also drilling and development. If time-lapse seismic surveys compare 3D seismic surveys, the time-lapse surveys are widely called as 4D seismic. A meaningful time-lapse interpretation is based on the repeatability of seismic surveys, which mainly depends on improved positioning and reduced noise (if surveys were designed properly through a feasibility study). The time-lapse interpretation can help oil and gas companies to maximize oil and gas recovery. This paper discusses about time-lapse seismic surveys mainly focused on feasibility, repeatability, data processing and interpretation.

• Journal of Korean Elementary Science Education
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• v.28 no.2
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• pp.142-153
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• 2009

The purpose of this study was to survey primary students' perception of the global and local environmental issues. The subjects were 853 fifth primary students in Seoul, Busan, Daejeon, Anyang, Gimpo and Cheongju. The results were as follows: First, the perception level of the global environmental issues were global warming (M=3.99), drinking-water pollution (M=3.92), acid rain (M=3.77), yellow dust (M=3.66), ozone depletion (M=3.57), deforestation in tropical areas (M=3.52), desertification (M=3.36), biodiversity (M=3.40) and their perception level of local environmental issues were waste disposal (M=3.87), air pollution (M=3.74), bad smell (M=3.70), noise (M=3.61), river pollution (M=3.57), soil pollution (M=3.44). Second, it was shown that the metropolitan students' perception levels of environmental issues were higher than the non-metropolitan students'. There wasn't a significant difference in the perception levels of two scale environmental issues and characteristics for sex (p>.05). Third, the result of analyzing on the effects between characteristics was that the factors like 'environmental interest' and 'impact on personal life' had a strong influence on the 'behavior will' to solve the global environmental issues, and 'environmental interest' and 'personal knowledge' had a strong influence on the 'behavior will' to solve the local environmental issues. Fourth, the result of analyzing on the characteristics according to the types of the environmental issues showed that 'personal knowledge', 'environmental interest' and 'behavior will' were high in the global environmental issues (p<.01) and 'virtual contamination', 'human responsibility' and 'impact on personal life' were high in the local environmental issues (p<.05).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.131-137
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• 2009

Binary offset carrier (BOC) signal synchronization is one of the most important steps to recover the transmitted information in global navigation satellite systems (GNSS) including Galileo and global positioning system (GPS). Generally, BOC signal synchronization is based on the correlation between the received and locally generated BOC signals. Thus, the multiple side-peaks in BOC autocorrelation are one of the main error sources in synchronizing BOC signals. Recently, a novel correlation function with reduced side-peaks was proposed for BOC signal synchronization by Julien [8]; however, Julien's correlation function not only still has the side-peaks, but also is only applicable to sine phased BOC(n, n), where n is the ratio of the pseudo random noise (PRN) code rate to 1.023 MHz. In this paper, we propose a new correlation function for BOC signal synchronization, which does not have any side-peaks and is applicable to general types of BOC signals, sine/cosine phased BOC(kn, n), where k is the ratio of a PRN chip duration to the period of a square wave sub-carrier used in BOC modulation. In addition, an efficient correlator structure is presented for generating the proposed correlation function.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.744-749
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• 2013

We propose soft user data input on LDPC codes with parity encoded by the (1, 7) run length limited (RLL) code for perpendicular magnetic recording channel. The user data are encoded by maximum transition run (MTR) (3;11) code. In order to minimize the loss of code rate, the (1, 7) RLL code only encode the parity of LDPC. Also, to increase performance, we propose only user data part applied soft output Viterbi algorithm (SOVA). The performance using the SOVA showed good performance lower than 26 dB. In contrast, it showed worse performance high than 26 dB. This is because of incorrect soft information by high jitter noise and two different input types for LDPC decoder.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.1
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• pp.148-158
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• 2011

As critical infrastructure, bridges play an indispensable role in facilitating the distribution of goods. When bridges reach their end of useful life or get damaged by natural disasters such as earthquakes or storms, they have to be removed and reconstructed. When bridges in service need to be reconstructed, user costs occur from vehicle detours and traffic congestions, and social costs occur from noise and dust during construction periods. However, these user and social costs are not considered during reconstruction and the evaluation methods of those costs are vague. Thus, there is lack of appropriate bridge types that consider these costs. Therefore, this paper identifies the social overhead costs that occur during bridge reconstruction, which is also called, users' socioeconomic values. Next, it proposes a method to evaluate user costs during bridge reconstruction, and appraises the method. User costs are evaluated based on traffic information, social and material volumes including the bridge's daily traffic volume, peak hours, detour distance and time. In addition, time delay costs due to traffic operational costs and bridge reconstruction are also taken into consideration.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.446-454
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• 2004

With the growth of multimedia systems, compressing image data has become more important in the area of multimedia services. Since a compressed image bitstream can often be seriously distorted by various types of channel noise, an error concealment algorithm becomes a very important issue. In order to solve this problem, Hsia proposed the error concealment algorithm where he recovered lost block data using 1D boundary matching vectors. His algorithm, however, requires high computational complexity since each matching vector needs MAD (Mean Absolute Difference) values of all pixels, which is either a boundary line top or a boundary line bottom of a damaged block. We propose a hierarchical search-based fast error concealment scheme as well as its approximated version to reduce computational time. In the proposed scheme, a hierarchical search is applied to reduce the number of checking points for searching a vector. The error concealment schemes proposed in this paper can be about 3 times faster than Hsia's with keeping visual quality and PSNR.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.6
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• pp.22-29
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• 2002

The conventional neon transformer systems are very bulky and heavy because it consist of leakage type transformers made of silicon steel plates. In addition, it has problems in noise by a neon transformer and in possibilities of fire and electrical shock when neon tubes are destroyed. A protection circuit is designed for all types of neon transformer loaded with one or more neon tubes. Whenever the neon tube fails to be started up, comes to the life end, encounters faults with open-circuits at the output terminals of the neon transformer, the protection circuit will be initiated to avoid more critical hazards. The input of the transformer is automatically cut off when the abnormal condition occurs, preventing waste of no-load power. To improve such problems, in this paper, a new type of neon power supply systems for neon tube is designed and implemented using inverter type circuits and a newly designed lightweight transformer. In the developed neon transformer system, a 60［Hz］power input is converted to 20［KHz］high frequency using half-wave inverters, thereby the transformer reduces its size by 1/5 in volume and 1/10 in weight.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Safety and Health at Work
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• v.6 no.4
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• pp.289-294
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• 2015

Background: The type of payment is one of the important factors that has an effect on the health of employees, as a basic working condition. In the conventional research field of occupational safety and health, only the physical, chemical, biological, and ergonomic factors are treated as the main hazardous factors. Managerial factors and basic working conditions such as working hours and the type of payment are neglected. This paper aimed to investigate the association of the type of payment and the exposure to the various hazardous factors as an heuristic study. Methods: The third Korean Working Conditions Survey (KWCS) by the Occupational Safety and Health Research Institute in 2011 was used for this study. Among the total sample of 50,032 economically active persons, 34,788 employees were considered for analysis. This study examined the relation between the three types of payment such as basic fixed salary and wage, piece rate, and extra payment for bad and dangerous working conditions and exposure to hazardous factors like vibration, noise, temperature, chemical contact, and working at very high speeds. Multivariate regression analysis was used to measure the effect of the type of payment on working hours exposed to hazards. Results: The result showed that the proportion of employees with a basic fixed salary was 94.5%, the proportion with piece rates was 38.6%, and the proportion who received extra payment for hazardous working conditions was 11.7%. Conclusion: The piece rate was associated with exposure to working with tight deadlines and stressful jobs. This study had some limitations because KWCS was a cross-sectional survey.