• Geomechanics and Engineering
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• v.17 no.4
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• pp.343-354
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• 2019

The present study demonstrates the application of seismic petrophysics and amplitude versus angle (AVA) forward modeling to identify the reservoir fluids, discriminate their saturation levels and natural gas composition. Two case studies of the Lumshiwal Formation (mainly sandstone) of the Lower Cretaceous age have been studied from the Kohat Sub-basin and the Middle Indus Basin of Pakistan. The conventional angle-dependent reflection amplitudes such as P converted P ($R_{PP}$) and S ($R_{PS}$), S converted S ($R_{SS}$) and P ($R_{SP}$) and newly developed AVA attributes (${\Delta}R_{PP}$, ${\Delta}R_{PS}$, ${\Delta}R_{SS}$ and ${\Delta}R_{SP}$) are analyzed at different gas saturation levels in the reservoir rock. These attributes are generated by taking the differences between the water wet reflection coefficient and the reflection coefficient at unknown gas saturation. Intercept (A) and gradient (B) attributes are also computed and cross-plotted at different gas compositions and gas/water scenarios to define the AVO class of reservoir sands. The numerical simulation reveals that ${\Delta}R_{PP}$, ${\Delta}R_{PS}$, ${\Delta}R_{SS}$ and ${\Delta}R_{SP}$ are good indicators and able to distinguish low and high gas saturation with a high level of confidence as compared to conventional reflection amplitudes such as P-P, P-S, S-S and S-P. In A-B cross-plots, the gas lines move towards the fluid (wet) lines as the proportion of heavier gases increase in the Lumshiwal Sands. Because of the upper contacts with different sedimentary rocks (Shale/Limestone) in both wells, the same reservoir sand exhibits different response similar to AVO classes like class I and class IV. This study will help to analyze gas sands by using amplitude based attributes as direct gas indicators in further gas drilling wells in clastic successions.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.591-602
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• 2003

Delay and discontinuance phenomenon of service are cause by sudden increase of the network communication amount and the quantity consumed of resources when Internet users are driven excessively to a conventional single large database sewer. To solve these problems, spatial database cluster consisted of several single nodes on high-speed network to offer high-performance is risen. But, research about spatial join operation that can reduce the performance of whole system in case process at single node is not achieved. So, in this paper, we propose efficient parallel spatial join processing method in a spatial database cluster system that uses data partitions and replications method that considers the characteristics of space data. Since proposed method does not need the creation step and the assignment step of tasks, and does not occur additional message transmission between cluster nodes that appear in existent parallel spatial join method, it shows performance improvement of 23% than the conventional parallel R-tree spatial join for a shared-nothing architecture about expensive spatial join queries. Also, It can minimize the response time to user because it removes redundant refinement operation at each cluster node.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.90-102
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• 2021

Technology evaluation is to assess the ability of technology commercialization entities to generate profits by using the subject technology, and domestic technology evaluation agencies have established and implemented their own evaluation systems. In particular, the recently developed technology evaluation model in the fields of marine and fishery does not sufficiently reflect the poor environment for technology development compared to other industries, so it does not pass the level of T4 rating, which is considered appropriate for investment. This is recognized as a challenge that occurs when the common evaluation indicators and evaluation scales used in other industries, and when the scoring system for T1 to T10 grading is similarly or identically utilized. Therefore, through this study, we intend to secure the appropriateness and reliability of the results of the comprehensive rating calculation by developing technology evaluation models and indicators that well explain the nine marine and fisheries industry classification systems. Based on KED and technology evaluation case data, AHP-based index weighting and Monte Carlo simulation-based rating system are applied and the results of case studies are verified. Through the proposed model, we aim to enhance the usability of R&D and commercialization support programs based on fast, convenient and objective evaluation results by applying to upcoming technology evaluation cases.

• Korean Management Science Review
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• v.31 no.1
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• pp.1-15
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• 2014

As the scarcity of natural resources has become apparent, the recycling industry has emerged as a promising one for its growth potential. Yet, the recycling industry still remains undeveloped and inefficient for various reasons. In this study, we focus on firms' recycling processes to understand the current status of recycling firms' value creation activities. With respect to the adopted recycling processes, we empirically investigate the differences in firm characteristics and firm performance. We use the data from Keco (Korea Environment Cooperation) which annually conducts a survey of recycling firms in Korea. We exclusively consider the whole group of plastic recycling industry in order to control for a possible bias in firm performance, stemming from the heterogeneity in processing and recycling of materials other than plastics. We review the descriptive statistics from the sample firms and conduct a series of hierarchical regression analyses. The results show that most of the firms in this industry adopt physical transformation processes with a low-level technology. These firms with physical transformation processes are smaller in size and produce entry level items which do not secure higher margins. The results indicate that the recycling industry largely comprises low value added firms which lack economies of scale and resources for R&D. For the stable growth of the industry, recycling firms must create sustainable values through implementation of technology-driven processes and improvement in product quality. In addition, the government should help build a reliable reverse logistic network, lower the entry barriers, and provide necessary funding for the SMEs in the recycling industry.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.261-274
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• 2019

This paper aims to develop an appropriate methodology for establishing an investment strategy for 'demonstration of artificial rainfall technology using UAV' and that include establishment of a technology classification, set of indicators for technology evaluation, suggestion of final key technology as a whole study area. It is designed to complement the latest research trend analysis results and expert committee opinions using quantitative analysis. The key indicators for technology evaluation consisted of three major items (activity, technology, marketability) and 10 detailed indicators. The AHP questionnaire was conducted to analyze the importance of indicators. As a result, it was analyzed that the attribute of the technology itself is most important, and the order of closeness to the implementation of the core function (centrality), feasibility (feasibility). Among the 16 technology groups, top investment priority groups were analyzed as ground seeding, artificial rainfall verification, spreading and diffusion of seeding material, artificial rainfall numerical modeling, and UAV sensor technology.

• ALGAE
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• v.30 no.2
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• pp.121-137
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• 2015

Studies on carbon flux in the oceans have been highlighted in recent years due to increasing awareness about climate change, but the coastal ecosystem remains one of the unexplored fields in this regard. In this study, the dynamics of carbon flux in a vegetative coastal ecosystem were examined by an evaluation of net and gross ecosystem production (NEP and GEP) and $CO_2$ exchange rates (net ecosystem exchange, NEE). To estimate NEP and GEP, community production and respiration were measured along different habitat types (eelgrass and macroalgal beds, shallow and deep sedimentary, and deep rocky shore) at Gwangyang Bay, Korea from 20 June to 20 July 2007. Vegetative areas showed significantly higher ecosystem production than the other habitat types. Specifically, eelgrass beds had the highest daily GEP ($6.97{\pm}0.02g\;C\;m^{-2}\;d^{-1}$), with a large amount of biomass and high productivity of eelgrass, whereas the outer macroalgal vegetation had the lowest GEP ($0.97{\pm}0.04g\;C\;m^{-2}\;d^{-1}$). In addition, macroalgal vegetation showed the highest daily NEP ($3.31{\pm}0.45g\;C\;m^{-2}\;d^{-1}$) due to its highest P : R ratio (2.33). Furthermore, the eelgrass beds acted as a $CO_2$ sink through the air-seawater interface according to NEE data, with a carbon sink rate of $0.63mg\;C\;m^{-2}\;d^{-1}$. Overall, ecosystem production was found to be extremely high in the vegetated systems (eelgrass and macroalgal beds), which occupy a relatively small area compared to the unvegetated systems according to our conceptual diagram of a carbon-flux box model. These results indicate that the vegetative ecosystems showed significantly high capturing efficiency of inorganic carbon through coastal primary production.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.458-465
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• 2020

Technological advances in artificial intelligence are affecting many industries, such as telecommunications, logistics, security, and healthcare, and research and development related to economic, efficiency, linkage with commercial technologies are the current focus. Predicting the changes in the future battlefield environment and ways of conducting war from a strategic point of view, as well as designing/planning the direction of military development for a leading response is not only a basic element to prepare for comprehensive future threats but also an indispensable factor that can produce an optimal effect over a limited budget/time. From this perspective, this study was conducted as part of a technology-driven plan to discover potential future technologies with high potential for use in the defense field and apply them to R&D. In this study, based on research data collected in a defense future technology investigation, the future new technology that requires further research was predicted by considering the redundancy with existing defense research projects and the feasibility of technology. In addition, an empirical study was conducted to verify the significance between the future new defense technology and the evaluation indicators in the AI field.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.