• New & Renewable Energy
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• v.19 no.2
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• pp.13-22
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• 2023

With the increased interest in renewable energy, various hydrogen production technologies have been developed. Hydrogen production can be classified into green, blue, gray, and pink hydrogen depending on the production method; each method has different technical performance, costs, and CO₂ emission characteristics. Hence, selecting the technology priorities that meet the company strategy is essential to develop technologically and economically feasible projects and achieve the national carbon neutrality targets. In addition, in early development technologies, analyzing the technology investment priorities based on the company's strategy and establishing investment decisions such as budget and human resources allocation is important. This study proposes a method of selecting priorities for various hydrogen production technologies as a specific implementation plan to achieve the national carbon neutrality goal. In particular, we analyze key performance indicators for technology, economic feasibility, and environmental performance by various candidate technologies and suggest ways to score them. As a result of the analysis using the aforementioned method, the priority of steam methane reforming (SMR) technology combined with carbon capture & storage (CCS) was established to be high in terms of achieving the national carbon neutrality goal.

• Journal of the Korean Society of Food Culture
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• v.24 no.5
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• pp.486-495
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• 2009

The purpose of this research was to investigate whether the perception of KPIs by restaurant managers from financial and non-financial perspectives was affected by BSC implementation. The perceptions that were examined were importance, adoption, performance, and utilization of KPIs. We surveyed managers from multinational restaurant chains that were adopting BSC and those that were not. From a non-financial perspective, the difference in perceived importance between BSC adopted firms and firms that did not adopt BSC was significant. The managers of BSC adopted firms perceived KPIs more seriously than the others. Secondly, according to the managers' working experiences, the difference of perceived utilization in the internal business process perspective was significant between BSC adopted firms and firms that did not adopt BSC. In addition, from the learning and growth perspective, the difference in perceived adoption and utilization between the two groups was significant. Finally, in the BSC adopted firms, the perceived importance of the managers affected the other perceptions like adoption and utilization from both the financial and non-financial perspectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2590-2608
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• 2023

The high-altitude and mobility characteristics of unmanned aerial vehicles (UAVs) have made them a key element of new radio systems, particularly because they can exceed the limits of terrestrial networks. However, at high altitudes, UAVs can be significantly affected by intercell interference at a high line-of-sight probability. To mitigate this drawback, we propose an algorithm that selects the optimal beam to reduce interference and maximize transmission efficiency. The proposed algorithm comprises two steps: constructing a user-location-based fingerprint database according to the user types presented herein and cooperative beam selection. Simulations were conducted using cellular cooperative downlink systems for analyzing the performance of the proposed method, and the signal-to-interference-plus-noise cumulative distribution function and spectral efficiency cumulative distribution function were used as performance analysis indicators. Simulation results showed that the proposed algorithm could reduce the effect of interference and increase the performance of the desired signal. Moreover, the algorithm could efficiently reduce overheads and system cost by reducing the amount of resources required for information exchange.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Clinical Endoscopy
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• v.55 no.3
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• pp.426-433
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• 2022

Background/Aims: Cholangiogram interpretation is not used as a key performance indicator (KPI) of endoscopic retrograde cholangiopancreatography (ERCP) training, and national societies recommend different minimum numbers per annum to maintain competence. This study aimed to determine the relationship between correct ERCP cholangiogram interpretation and experience. Methods: One hundred fifty ERCPists were surveyed to appropriately interpret ERCP cholangiographic findings. There were three groups of 50 participants each: "Trainees," "Consultants group 1" (performed >75 ERCPs per year), and "Consultants group 2" (performed >100 ERCPs per year). Results: Trainees was inferior to Consultants groups 1 and 2 in identifying all findings except choledocholithiasis outside the intrahepatic duct on the initial or completion/occlusion cholangiogram. Consultants group 1 was inferior to Consultants group 2 in identifying Strasberg type A bile leaks (odds ratio [OR], 0.86; 95% confidence interval [CI], 0.77-0.96), Strasberg type B (OR, 0.84; 95% CI, 0.74-0.95), and Bismuth type 2 hilar strictures (OR, 0.81; 95% CI, 0.69-0.95). Conclusions: This investigation supports the notion that cholangiogram interpretation improves with increased annual ERCP case volumes. Thus, a higher annual volume of procedures performed may improve the ability to correctly interpret particularly difficult findings. Cholangiogram interpretation, in addition to bile duct cannulation, could be considered as another KPI of ERCP training.

• Animal Bioscience
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• v.37 no.7
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• pp.1213-1224
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• 2024

Objective: Enterotypes (ETs) are the clustering of gut microbial community structures, which could serve as indicators of growth performance and carcass traits. However, ETs have been sparsely investigated in waterfowl. The objective of this study was to identify the ileal ETs and explore the correlation of the ETs with growth performance and carcass traits in Muscovy ducks. Methods: A total of 200 Muscovy ducks were randomly selected from a population of 5,000 ducks at 70-day old, weighed and slaughtered. The growth performance and carcass traits, including body weight, dressed weight and evidenced weight, dressed percentage, percentage of apparent yield, breast muscle weight, leg muscle weight, percentage of leg muscle and percentage of breast muscle, were determined. The contents of ileum were collected for the isolation of DNA and 16S rRNA gene sequencing. The ETs were identified based on the 16S rRNA gene sequencing data and the correlation of the ETs with growth performance and carcass traits was performed by Spearman correlation analysis. Results: Three ETs (ET1, ET2, and ET3) were observed in the ileal microbiota of Muscovy ducks with significant differences in number of features and α-diversity among these ETs (p<0.05). Streptococcus, Candida Arthritis, and Bacteroidetes were the presentative genus in ET1 to ET3, respectively. Correlation analysis revealed that Lactococcus and Bradyrhizobium were significantly correlated with percentage of eviscerated yield and leg muscle weight (p<0.05) while ETs were found to have a close association with percentage of eviscerated yield, leg muscle weight, and percentage of leg muscle in Muscovy ducks. However, the growth performance of ducks with different ETs did not show significant difference (p>0.05). Lactococcus were found to be significantly correlated with leg muscle weight, dressed weight, and percentage of eviscerated yield. Conclusion: Our findings revealed a substantial variation in carcass traits associated with ETs in Muscovy ducks. It is implied that ETs might have the potential to serve as a valuable biomarker for assessing duck carcass traits. It would provide novel insights into the interaction of gut microbiota with growth performance and carcass traits of ducks.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2008.10a
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• pp.101-114
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• 2008

An efficient mine management system demand constant attention of mine managers on the key performance indicators like production targets, equipment status, condition of haul roads, safety etc.. There is a wealth of information generated during day to day working of the mine. The success of a mining enterprise is a function of reliability of accumulated information and decision making on the basis of such information. In the present paper a computerized mine operations management system developed for a large opencast mine making use of the potential benefits of geographical information systems, real time kinematic global positioning systems and a communication network to improve the overall efficiency of the mine is presented.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.121-128
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• 2013

Because of governmental regulations, large-scale discount stores are trying to find a new way that can expand a market size. Homeplus virtual store has received attention from the other companies, because virtual store can access 24 hours and be unfettered by governmental regulations. The purpose of the study is to review performance and strategies of Homeplus virtual store as well as the cause of the failure of imitators. To do this, we can find key indicators of success of virtual store.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.466-482
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• 2015

IT is the key enabler of digital economy. Appropriate usage of IT can provide a strategic competitive advantage to a firm in a dynamic competitive environment. However, there has been a continuing debate on whether IT can actually enhance the productivity of firms. This concept is called IT productivity paradox. In this study, we analyzed the causality among appropriate indicators to demonstrate the real impact of IT on productivity. The 12,100 sample data from 2011 were used for analysis. As expected, the results indicated that mobile device usage, website adoption, e-commerce, open source, cloud computing, and green computing positively influence IT productivity. This unprecedented large-scale analysis can provide clarification regarding the ambiguous causal mechanism between IT usage and productivity.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.33-45
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• 2013

The trend toward ubiquitous computing does not represent simply a change in the way people access and use information. In the end it will have a profound effect on the way people work with each other. In this paper, we have defined functionalities of an information system that helps operate a kind of smart office environment, and implemented the system using RFID technology. The system provides three major functions-time and attendance management, personalized message notification, and flexible seat allocation for a smart working center. We have also suggested the key performance indicators and performed a KPI analysis to measure an efficiency of implemented system.