• Wind and Structures
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• v.32 no.2
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• pp.169-177
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• 2021

This study investigates the effects of blade configuration and solidity of Darrieus wind turbine on the starting torque characteristics. Generally, the configuration of Darrieus wind turbine is divided into Troposkien, parabola, Catenary, Sandia, modified-parabola and straight types. A numerical analysis has been carried out using Multiple Stream Tube (MST) method to investigate the effect of blade configuration and solidity of Darrieus wind turbine on the starting torque under the initial low range of rotational speed. The simulation results show that the starting torque of Darrieus wind turbine varies considerably depending on the blade configuration. The initial starting torque was larger with Troposkien, Parabola, Catenary, and Sandia configurations than with modified parabola or straight types. The increase in solidity with increasing number of blades raised the starting torque and improved the dynamic stability during the initial operational speed of Darrieus wind turbine. Additionally, these torque results represent basic data for fluid-structure interaction (FSI) simulation of the steady-dynamic operation of the turbine.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.397-419
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• 2020

Business analytics is a management tool for achieving significant business performance improvements. Many organizations fail to or only partially achieve their business objectives and goals from business analytics. Business analytics adoption is a multi-stage complex activity consisting of evaluation, adoption, and assimilation stages. Several research papers have been published in the field of business analytics, but the research on multi-stage BA adoption is fewer in number. This study contributes to the scant literature on the multi-stage adoption model by identifying the critical themes for evaluation, adoption, and assimilation stages of business analytics. This study uses the thematic content analysis of peer-reviewed published academic papers as a research technique to explore the key themes of business analytics adoption. This study links the critical themes with the popular theoretical foundations: Resource-Based View (RBV), Dynamic Capabilities, Diffusion of Innovations, and Technology-Organizational-Environmental (TOE) framework. The study identifies twelve major factors categorized into three key themes: organizational characteristics, innovation characteristics, and environmental characteristics. The main organizational factors are top management support, organization data environment, centralized analytics structure, perceived cost, employee skills, and data-based decision making culture. The major innovation characteristics are perceived benefits, complexity, and compatibility, and information technology assets. The environmental factors influencing BA adoption stages are competition and industry pressure. A conceptual framework for the multi-stage BA adoption model is proposed in this study. The findings of this study can assist the practicing managers in developing a stage-wise operational strategy for business analytics adoption. Future research can also attempt to validate the conceptual model proposed in this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.4
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• pp.25-34
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• 2010

This study was conducted to develop evaluation indicators to improve athletic performance and operational management of golf courses and the results of the research are as follows. Through theoretical research and a preliminary professional survey, 15 on-going evaluations of golf course composition and operational management and 55 sub-evaluation indices were rejected while 10 on-going evaluations and 52 sub-evaluation indicators were reconfigured as final for environmental-friendliness, level of member services, level of human service of game personnel, difficulties of course, management level of the course, fairness of operational management, accessibility and location characteristic, traditions and ambiance of the golf club, quality of course, and course layout. When analyzing the important decision factors in golf course user preference evaluation indicators, the following contributed in the order of higher to lower contributions: the management level of the course, excellence of the course, level of human services for personnel, course layout and environmental-friendliness. When identifying the path coefficient of golf course evaluation indicators, the curvature of a hole and the length of the course had a causal effect on the 'course layout' section. Tournament facilities and various shot values had a causal relationship with 'excellence of the course', in the order of higher to lower, and convenience of waiting and fair allocation of reservations for 'fairness of operational management'. The history of the golf course and its environmental characteristics, history and culture of the region have relatively higher causal effects on 'traditions of the golf club' and geographical conditions on 'accessibility and location characteristics', pesticide and fertilizer usage and water pollution on 'environmental-friendliness', and member benefit and kindness of employees on 'level of member services'. The kindness and expertise of the game personnel had a relatively higher causal effect on the 'level of human services of game personnel', the location of tenning area, and location of OB and hazards on 'difficulties of course', and rough conditions and obstacles management on 'management level of the course'. There is a need to complete a systematic evaluation index system for golf course user preferences through future studies for a more detailed assessment, as well as a process to verify these evaluation indicators by application to domestic and international golf courses.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Journal of Environmental Science International
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• v.30 no.12
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• pp.1015-1025
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• 2021

Our research work aimed to evaluate cover crop effects of winter rye on soil characteristics, soil conservation, and yield productivities on potato fields with 15% slope during a fallowed period. There were two controls of bared field without any cultivation and conventional potato cultivation without winter rye. Potato cultivation increased soil pH, organic matter, available phosphate, and exchangeable cation regardless of cover crop cultivation. Sub-soil, particularly, all components of soil chemical properties showed higher value in winter rye cultivation than conventional cultivation. Higher soil density was observed on cover crop cultivation than conventional cultivation resulting from root residues of the cover crop both topsoil and subsoil. Cover crop residues positively affected plant growth and reduced the amount of soil erosion by holding the soil. Although severe soil erosion was seen in conventional cultivation, winter rye cultivation declined soil erosion by 47% during the fallow period on potato slope fields. Distinct soil bacterial communities were detected among treatments and some OTU(Operational Taxonomic Unit)s showed significantly higher abundance in winter rye treatment. Total yield and commercial rate demonstrated no significant differences while higher tuber phosphate, K⁺, and Mg²⁺ contents were observed in winter rye cultivation.

• Korean Journal of Environmental Agriculture
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• v.25 no.3
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• pp.195-201
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• 2006

The operational characteristics of anaerobic bioreactors in treating swine wastewater were evaluated upto hydraulic retention time (HRT) of 1 day and organic loading rate (OLR) of $5.1kg-COD/m^3{\cdot}d$ for 200 days. The bioreactors were effective in treating swine wastewaters with COD removal efficiency of $78.9{\sim}81.5%$ and biogas generation of $0.39{\sim}0.59m^3/kg-COD_r$ at OLR of $1.1{\sim}2.2kg-COD/m^3{\cdot}d$. The two-stage ASBF anaerobic bioreactors was effective in treating different characteristics of swine wastewaters since they showed high and stable COD removal efficiency at high OLR due to effective retention of biomass by media and staging. The effects of ammonia on anaerobic digestion were investigated by operating two-stage ASBF reactors using swine wastewaters as influent without and with ammonia removal at HRT of $1{\sim}2$ days and OLR of $2.2{\sim}9.6kg-COD/m^3{\cdot}d$ for 250 days. The COD removal efficiency and biogas generation of two-stage ASBF reactors was decreased by increasing influent ammonia concentrations to 1,580 mg (T-N)/L with increasing OLR to $6.3kg-COD/m^3{\cdot}d$, while those were increased by maintaining influent ammonia concentrations below 340 mg (T-N)/L by MAP precipitation with increasing OLR to $9.6kg-COD/m^3{\cdot}d$. Initial inhibition of ammonia on anaerobic processes was observed at a concentration of 760 mg (T-N)/L and the COD removal efficiency and biogas generation dropped to 1/2 at ammonia concentration ranges of $1,540{\sim}1,870mg$ (T-N)/L. It is essential to remove ammonia in swine wastewaters to an initial inhibition level before anaerobic processes for the effective removal of COD.

• Smart Structures and Systems
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• v.23 no.3
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• pp.295-306
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• 2019

Shanghai Tower is a 632-meter super high-rise building located in an area with wind and active earthquake. A sophisticated structural health monitoring (SHM) system consisting of more than 400 sensors has been built to carry out a long-term monitoring for its operational safety. In this paper, a reduced-order model including 31 elements was generated from a full model of this super tall building. An iterative regularized matrix method was proposed to tune the system parameters, making the dynamic characteristic of the reduced-order model be consistent with those in the full model. The updating reduced-order model can be regarded as a benchmark model for further analysis. A long-term monitoring for structural dynamic characteristics of Shanghai Tower under different construction stages was also investigated. The identified results, including natural frequency and damping ratio, were discussed. Based on the data collected from the SHM system, the dynamic characteristics of the whole structure was investigated. Compared with the result of the finite element model, a good agreement can be observed. The result provides a valuable reference for examining the evolution of future dynamic characteristics of this super tall building.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.543-552
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• 2018

The purpose of this study is to provide basic data for the improvement of the tournament operating environment and effective tournament operation. For the factor analysis, exploratory factor analysis was conducted. Based on this, in order to conduct hierarchical analysis, we've produced and distributed the questionnaire to expert group. And after, with collected data, AHP (Analytical Hierarchy Process) technique was used to analyze relative importance and determinants. As a result, operational factors showed the highest priority among the components of evaluation. And next prior factors are satisfaction of participants, environmental factors and etc. In order to stabilize the operation of the tournament, priority shall be securing the budget. And it is necessary to understand the desire of participants and shall consider characteristics and level of the tournament. In future studies, it will be necessary to make a comparative study through expert group in order to secure the fitness.

• Clean Technology
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• v.13 no.3
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• pp.222-227
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• 2007

The photocatalytic degradation of livestock wastewater has been investigated over $TiO_2$ photocatalysts irradiated with a ultraviolet (UV) light. The effect of operational parameters, i.e., distance, reaction area, concentration of suspended solids(SS), and column diameter on the degradation of livestock wastewater has been performed in lab-scale. The optimal conditions for livestock wastewater were determined: distance was 3 cm (less than 7 cm), reaction area was $3.6\;m^2$, SS concentration was 40 mg/L (less than 300 mg/L) and column diameter was 5 mm (less than 10 mm). Under the optimal conditions, COD, color and coliform removal efficiencies were approximately 49%, 53% and 100%, respectively. Non-biodegradable COD removal efficiency increased with 57% using by photocatalysis process. Therefore, it is shown that photocatalysis has an effect on degradation of non-biodegradable organic matter.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.