• Membrane Journal
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• v.33 no.4
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• pp.181-190
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• 2023

Wastewater purification is one of the most important techniques for controlling environmental pollution and fulfilling the demand for freshwater supply. Various technologies, such as different types of distillations and reverse osmosis processes, need higher energy input. Capacitive deionization (CDI) is an alternative method in which power consumption is deficient and works on the supercapacitor principle. Research is going on to improve the electrode materials to improve the efficiency of the process. A reverse electrodialysis (RED) is the most commonly used desalination technology and osmotic power generator. Among many studies conducted to enhance the efficiency of RED, MXene, as an ion exchange membrane (IEM) and 2D nanofluidic channels in IEM, is rising as a promising way to improve the physical and electrochemical properties of RED. It is used alone and other polymeric materials are mixed with MXene to enhance the performance of the membrane further. The maximum desalination performances of MXene with preconditioning, Ti₃C₂T_x, Nafion, and hetero-structures were respectively measured, proving the potential of MXene for a promising material in the desalination industry. In terms of osmotic power generating via RED, adopting MXene as asymmetric nanofluidic ion channels in IEM significantly improved the maximum osmotic output power density, most of them surpassing the commercialization benchmark, 5 Wm^-2. By connecting the number of unit cells, the output voltage reaches the point where it can directly power the electronic devices without any intermediate aid. The studies around MXene have significantly increased in recent years, yet there is more to be revealed about the application of MXene in the membrane and osmotic power-generating industry. This review discusses the electrodialysis process based on MXene composite membrane.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.587-602
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• 2023

Due to climate change, population growth, and economic development, the demand for water in the urban water system (UWS) and the energy required for water use constantly increase. Therefore, beyond the traditional method of considering only the water sector, the Nexus approach, which considers synergies and trade-offs between the water and energy sectors, has begun to draw attention. In previous researches, the Nexus methodology was used to demonstrate that the UWS is an energy-intensive system, analyze the water-energy efficiency relationship surrogated by energy intensity, and identify climate (long-term climate change, drought, type), geographic characteristics (topography, flat ratio, location), system characteristics (total supply water amount, population density, pipeline length), and operational management level (water network pressure, leakage rate, water saving) effects on the UWS. Through this, it was possible to suggest the direction of policies and institutions to UWS managers. However, there was a limit to establishing and implementing specific action plans. This study built the energy intensity matrix of the UWS, quantified the impact of city conditions, external influences, and operational management levels on the UWS using the water-energy Nexus model, and introduced water-energy efficiency criteria. With this, UWS managers will be able to derive strategies and action plans for efficient operation management of the UWS and evaluate suitability and validity after implementation.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.245-259
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• 2023

The agricultural reservoir is a critical water supply system in South Korea, providing approximately 60% of the agricultural water demand. However, the reservoir faces several issues that jeopardize its efficient operation and management. To address this issues, we propose a novel deep-learning-based water level recognition model that uses CCTV image data to accurately estimate water levels in agricultural reservoirs. The model consists of three main parts: (1) dataset construction, (2) image segmentation using the U-Net algorithm, and (3) CCTV-based water level recognition using either CNN or ResNet. The model has been applied to two reservoirs G-reservoir and M-reservoir with observed CCTV image and water level time series data. The results show that the performance of the image segmentation model is superior, while the performance of the water level recognition model varies from 50 to 80% depending on water level classification criteria (i.e., classification guideline) and complexity of image data (i.e., variability of the image pixels). The performance of the model can be improved if more numbers of data can be collected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.76-85
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• 2023

Demand for hydrogen as a renewable energy resource is increasing. However, unlike conventional fossil fuels, hydrogen requires a dedicated refueling station for fuel supply. A risk assessment of hydrogen refueling stations must be undertaken to secure the infrastructure. Therefore, in this study, a risk assessment for hydrogen refueling stations was conducted through both qualitative and quantitative risk assessments. For the qualitative evaluation, the hydrogen dispenser module was evaluated as two nodes using the hazard and operability (HAZOP) analysis. The risk due to filter clogging and high-pressure accidents was evaluated to be high according to the criticality estimation matrix. For the quantitative risk assessment, the Hydrogen Korea Risk Assessment Module (Hy-KoRAM) was used to indicate the shape of the fire and the range of damage impact, and to evaluate the individual and social risks. The individual risk level was determined of to be as low as reasonably practicable (ALARP). Additional safety measures proposed include placing the hydrogen refueling station about 100m away from public facilities. The social risk level was derived as 1E-04/year, with a frequency of approximately 10 deaths, falling within the ALARP range. As a result of the qualitative and quantitative risk assessments, additional safety measures for the process and a safety improvement plan are proposed through the establishment of a restricted area near the hydrogen refueling station.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.1-19
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• 2023

Bikes have recently emerged as an alternative to carbon neutrality. To understand the demand for public bikes, we endeavored to estimate travel frequency of public bike by considering the intermediate stops. Using the GPS trajectory data of 'Ttareungyi', a public bike service in Seoul, we identified a stay point and estimated travel frequency reflecting population, land use, and physical characteristics. Application of map matching and a stay point detection algorithm revealed that stay point appeared in about 12.1% of the total trips. Compared to a trip without stay point, the trip with stay point has a longer average travel distance and travel time and a higher occurrence rate during off-peak hours. According to visualization analysis, the stay points are mainly found in parks, leisure facilities, and business facilities. To consider the stay point, the unit of analysis was set as a hexagonal grid rather than the existing rental station base. Travel frequency considering the stay point were analyzed using the Zero-Inflated Negative Binomial (ZINB) model. Results of our analysis revealed that the travel frequency were higher in bike infrastructure where the safety of bike users was secured, such as 'Bikepath' and 'Bike and pedestrian path'. Also, public bikes play a role as first & last mile means of access to public transportation. The measure of travel frequency was also observed to increase in life and employment centers. Considering the results of this analysis, securing safety facilities and space for users should be given priority when planning any additional expansion of bike infrastructure. Moreover, there is a necessity to establish a plan to supply bike infrastructure facilities linked to public transportation, especially the subway.

• 한국체육학회지인문사회과학편
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• v.57 no.6
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• pp.123-134
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• 2018

The purpose of this study is to analyze the economic efficiency of the policy implemented by Ministry of Culture, Sports and Tourism on leading company in sport industry. The leading companies in sport industry are those who have a certain amount of sales in sport industry and the ones with potential to become global companies. Supporting areas include business advancement, overseas market development, and overseas PR marketing integration support. The research is performed by developing the equilibrium model composed of supply as well as demand and applying input-output analysis. The economic efficiency is estimated to in the form of changes in the sales of corporations and the ripple effect of the national economy. The results of the study are as follows. First, it is estimated that the sales growth rate of the company due to the implementation of the policy is from 3.74% to 5.19%. Second, the increase in sales reaches to a maximum of KRW 4,081 billion with a minimum of KRW 1,573 million, depending on the size of the company. Third, it is estimated that the production inducement effect for the national economy is from KRW 36 billion to KRW 93.4 billion. Fourth, the induced value added for the national economy is estimated to be at least KRW 11.3 billion, up to KRW 29.2 billion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.128-130
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• 2021

This study aims to implement a Regression Analysis system for predicting the appropriate production of flatfish. Due to Korea's signing of FTAs with countries around the world and accelerating market opening, Korean flatfish farming businesses are experiencing many difficulties due to the specificity and uncertainty of the environment. In addition, there is a need for a solution to problems such as sluggish consumption and price drop due to the recent surge in imported seafood such as salmon and yellowtail and changes in people's dietary habits. in this study, Using the python module, xlwings, it was used to obtain for the production amount of flatfish and to predict the amount of flatfish to be produced later. was used to predict the amount of flatfish to be produced in the future. Therefore, based on the analysis results of this prediction of flatfish production, the flatfish aquaculture industry will be able to come up with a plan to achieve an appropriate production volume and control supply and demand, which will reduce unnecessary economic loss and promote new value creation based on data. In addition, through the data approach attempted in this study, various analysis techniques such as artificial neural networks and multiple regression analysis can be used in future research in various fields, which will become the foundation of basic data that can effectively analyze and utilize big data in various industries.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.1-20
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• 2022

As the global business environment is rapidly changing due to the 4th industrial revolution, new jobs that did not exist before are emerging. Among them, the job that companies are most interested in is 'Data Scientist'. As information and communication technologies take up most of our lives, data on not only online activities but also offline activities are stored in computers every hour to generate big data. Companies put a lot of effort into discovering new opportunities from such big data. The new job that emerged along with the efforts of these companies is data scientist. The demand for data scientist, a promising job that leads the big data era, is constantly increasing, but its supply is not still enough. Although data analysis technologies and tools that anyone can easily use are introduced, companies still have great difficulty in finding proper experts. One of the main reasons that makes the data scientist's shortage problem serious is the lack of understanding of the data scientist's job. Therefore, in this study, we explore the job competencies of a data scientist by qualitatively analyzing the actual job posting information of the company. This study finds that data scientists need not only the technical and system skills required of software engineers and system analysts in the past, but also business-related and interpersonal skills required of business consultants and project managers. The results of this study are expected to provide basic guidelines to people who are interested in the data scientist profession and to companies that want to hire data scientists.

• Asia Marketing Journal
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• v.16 no.1
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• pp.35-69
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• 2014

The impact of competition on pricing has been studied in the context of counterfactual merger analyses where expected optimal prices in a hypothetical monopoly are compared with observed prices in an oligopolistic market. Such analyses would typically assume static decision making by consumers and firms and thus have been applied mostly to data obtained from consumer packed goods such as cereal and soft drinks. However such static modeling approach is not suitable when decision makers are forward looking. When it comes to the markets for durable products with indirect network effects, consumer purchase decisions and firm pricing decisions are inherently dynamic as they take into account future states when making purchase and pricing decisions. Researchers need to take into account the dynamic aspects of decision making both in the consumer side and in the supplier side for such markets. Firms in a two-sided market typically subsidize one side of the market to exploit the indirect network effect. Such pricing behaviors would be more prevalent in competitive markets where firms would try to win over the battle for standard. While such qualitative expectation on the relationship between pricing behaviors and competitive structures could be easily formed, little empirical studies have measured the extent to which the distinct pricing structure in two-sided markets depends on the competitive structure of the market. This paper develops an empirical model to measure the impact of competition on optimal pricing of durable products under indirect network effects. In order to measure the impact of exogenously determined competition among firms on pricing, we compare the equilibrium prices in the observed oligopoly market to those in a hypothetical monopoly market. In computing the equilibrium prices, we account for the forward looking behaviors of consumers and supplier. We first estimate a demand function that accounts for consumers' forward-looking behaviors and indirect network effects. And then, for the supply side, the pricing equation is obtained as an outcome of the Markov Perfect Nash Equilibrium in pricing. In doing so, we utilize numerical dynamic programming techniques. We apply our model to a data set obtained from the U.S. video game console market. The video game console market is considered a prototypical case of two-sided markets in which the platform typically subsidizes one side of market to expand the installed base anticipating larger revenues in the other side of market resulting from the expanded installed base. The data consist of monthly observations of price, hardware unit sales and the number of compatible software titles for Sony PlayStation and Nintendo 64 from September 1996 to August 2002. Sony PlayStation was released to the market a year before Nintendo 64 was launched. We compute the expected equilibrium price path for Nintendo 64 and Playstation for both oligopoly and for monopoly. Our analysis reveals that the price level differs significantly between two competition structures. The merged monopoly is expected to set prices higher by 14.8% for Sony PlayStation and 21.8% for Nintendo 64 on average than the independent firms in an oligopoly would do. And such removal of competition would result in a reduction in consumer value by 43.1%. Higher prices are expected for the hypothetical monopoly because the merged firm does not need to engage in the battle for industry standard. This result is attributed to the distinct property of a two-sided market that competing firms tend to set low prices particularly at the initial period to attract consumers at the introductory stage and to reinforce their own networks and eventually finally to dominate the market.

• Korean small business review
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• v.39 no.3
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• pp.57-76
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• 2017

While the unemployment rate is rising rapidly due to recent economic recession at home and abroad, university students' reluctance to apply for jobs in Small and Medium Enterprises (SME's) causes instability in manpower supply and demand and social unrest. To provide insights for solving the problem, this study explores how beliefs and attitudes of university students influence their intention to apply for jobs in SME's using Theory of Planned Behavior proposed by Icek Ajzen. This study followed the 2-stage survey methodology suggested by Ajzen. In the first stage of pilot study, a small sample of university students was used to illicit readily accessible behavioral outcomes, normative referents, and control factors. In the second stage of main study, the standard questionnaire was designed and administered and data were collected and analysed using the PLS Structural Equation Modeling (SEM) technique. PLS-SEM was used instead of Covariance Based (CB)- SEM considering the exploratory nature of this study. In overall, the results showed that TPB is very effective in explaining and predicting the university student's intention to apply for jobs in SEM's. Gender turned out to be a significant moderator variable in the relations between intention and its influence factors. Student's scholastic performance showed a negative correlation with intention. More research efforts need to be exerted to better understand university student's job seeking behavior.