• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.137-146
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• 2012

In SCM(Supply Chain Management), a management paradigm where the customer satisfaction is to be achieved by minimizing the cost, reducing the uncertainty, and obtaining the overall optimization. As it performs the integrated operation of the paths of information, assets, and knowledge from the raw material providers to the retailers, the adoption of RFID(Radio Frequency Identification) in SCM could be expected to magnify the effectiveness of the system. However, there is a huge risk by deciding whether or not RFID system is adopted without the objective analysis under the uncertain circumstances. This research paper presents the statistical analysis methodologies for the comparison of RFID with Barcode on the aspect of utility and the statistical analysis tool, RUSAT, which was programmed for nonstatisticians' convenience. Assuming a pharmaceutical industry, this paper illustrates how the data were entered and analyzed in RUSAT. The results of this research are expected to be used not only for the pharmaceutical related company but also for the manufacturer, the whole-saler, and the retailer in the other logistic industries.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.1-22
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• 2018

The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

• BMB Reports
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• v.34 no.5
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• pp.440-445
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• 2001

Pesticide waste and chemical stockpiles are posing a potential threat to both Vie environment and human health. There is currently a great effort toward developing effective and economical methods for the detoxification of these toxic organophosphates. In terms of safety and economy, enzymatic biodegradation has been recommended as the most promising tool to detoxify these toxic materials. To develop an enzymatic degradation method to detoxify such toxic organophosphorus compounds, a gene encoding organophosphorus acid anhydrolase (OPAA) from genomic DNA of Alteromonas haloplanktis C was subcloned and expressed. The enzyme consists of a single polypeptide chain with a molecular weight of 48 kDa. It demonstrates strong hydrolyzing activity on sarin, an acetylcholinesterase inhibitor. Moreover, its high activity is sustained for a considerable length of time. It is projected that the recombinant OPAA can be applied as an enzymatic tool that can be used not only for the detoxification of pesticide wastes, but also for the demilitarization of chemical stockpiles.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.324-329
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• 2015

Though the supplier diversification is considered as a vital tool to mitigate the risk due to supply chain disruptions, there are results which show the optimality of the sole sourcing. This paper further generalizes the results to show that the sole sourcing is optimal under very mild conditions. Discussion on why the sole sourcing is optimal is given with the insight on the value of supplier diversification.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• The Journal of Information Systems
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• v.17 no.3
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• pp.205-233
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• 2008

This study suggests the dynamic value chain model, that will be able to not only show changing processes to organization's significant capital by integrating an individual, implicit, and explicit knowledge which affect organizational decision making, but also distinguish the key driver for raising organizational competitive power because it makes possible to analyze sensitivity of performance along with decision making alternatives and policy changes from dynamic view by connecting knowledge management capability, knowledge management activity, and relations with organizational performance with specific strategic map. Recently, a lot of organizations show interest in measuring and evaluating their performance synthetically. In organizations taking knowledge management, they introduce effective value chain model like a dynamic balanced scorecard (DBSC), and therefore they can reflect their knowledge management condition as well as show their changes by checking performance of established vision and strategy periodically. Furthermore, they can ask for their inner members' understanding and participation by communicating with and inspiring their members with awareness that members are one of their group, present a base of benchmarking, and offer significant information for later decision making. The BSC has been a successful framework for measuring an organization's performance in various perspectives through translating an organization's vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, this study employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC would serve as a useful strategic teaming tool for facilitating an organization's communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, this study applied the DBSC model to Prototype of Korea manufacturing and service firm.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.614-621
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• 2011

This paper presents design of a wide tuning range digitally controlled oscillator(DCO) for Mobile-DTV applications. DCO is the key element of the ADPLL block that generates oscillation frequencies. We proposed a binary delay chain(BDC) structure, for wide tuning range DCO, modifying conventional fixed delay chain. The proposed structure generates oscillation frequencies by delay cell combination which has a variable delay time of $2^i$ in the range of $0{\leq}i{\leq}n-1$. The BOC structure can reduce the number of delay cells because it make possible to select delay cell and resolution. We simulated the proposed DCO by Cadence's Spectre RF tool in 1.8V chartered $0.18{\mu}m$ CMOS process. The simulation results showed 77MHz~2.07GHz frequency range and 3ps resolution. The phase noise yields -101dBc/Hz@1MHz at Mobile-DTV maximum frequency 1675MHz and the power consumption is 5.87mW. The proposed DCO satisfies Mobile-DTV standards such as ATSC-M/H, DVB-H, ISDB-T, T-DMB.

• International Journal of Fluid Machinery and Systems
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• v.8 no.3
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• pp.142-154
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• 2015

Nowadays, computational fluid dynamics is commonly used by design engineers to evaluate and compare losses in hydraulic components as it is less expensive and less time consuming than model tests. For that purpose, an automatic tool for casing and distributor analysis will be presented in this paper. An in-house mesh generator and a Reynolds Averaged Navier-Stokes equation solver using the standard $k-{\omega}$ shear stress transport (SST) turbulence model will be used to perform all computations. Two solvers based on the C++ OpenFOAM library will be used and compared to a commercial solver. The performance of the new fully coupled block solver developed by the University of Lucerne and Andritz will be compared to the standard 1.6ext segregated simpleFoam solver and to a commercial solver. In this study, relative comparisons of different geometries of casing and distributor will be performed. The present study is thus aimed at validating the block solver and the tool chain and providing design engineers with a faster and more reliable analysis tool that can be integrated into their design process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2417-2430
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• 2024

The need for secure user authentication in blockchain-based applications has been growing with the increased adoption of Decentralized Identity (DID) credentials in blockchain. Zokrates, a tool designed to protect user privacy within smart contracts, had a limitation in that it could not accept authenticated user information such as credentials, only allowing the use of manually inputted data. In this paper, we propose a smart contract system that securely validates DID credentials to overcome the limitations of traditional centralized authentication systems. This system ensures the safe identification of users within blockchain-based applications by authenticating their identities in a trusted manner within the blockchain. As the demand for user authentication in blockchain rises, this paper emphasizes the significance of a blockchain-based identity verification system that guarantees both privacy and security. Leveraging the Zero-Knowledge Proof method and utilizing the Zokrates tool, this innovative approach aims to provide solutions for the digital identity verification process, thereby expanding the scope of blockchain technology applications. Moreover, we also provide a CLI for each entity. We help anyone who wants to authenticate their identity using the tool to safely verify it on-chain.

• Journal of Soil and Groundwater Environment
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• v.10 no.5
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• pp.52-60
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• 2005

The conceptual model of under-sampled study area will include a great amount of uncertainty. In this study, we investigate the applicability of Markov chain model in a spatial domain as a tool for minimizing the uncertainty arose from the lack of data. A new formulation is developed to generalize the previous two-dimensional coupled Markov chain model, which has more versatility to fit any computational sequence. Furthermore, the computational algorithm is improved to utilize more conditioning information and reduce the artifacts, such as the artificial parcel inclination, caused by sequential computation. A generalized 20 coupled Markov chain (GCMC) is tested through applying a hypothetical soil map to evaluate the appropriateness as a substituting model for conventional geostatistical models. Comparing to sequential indicator model (SIS), the simulation results from GCMC shows lower entropy at the boundaries of indicators which is closer to real soil maps. For under-sampled indicators, however, GCMC under-estimates the presence of the indicators, which is a common aspect of all other geostatistical models. To improve this under-estimation, further study on data fusion (or assimilation) inclusion in the GCMC is required.