• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.29-38
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• 2023

Doodles, often possess irregular shapes and patterns, making it challenging for artificial intelligence to mechanically recognize and predict patterns in random doodles. Unlike humans who can effortlessly recognize and predict doodles even when circles are imperfect or lines are not perfectly straight, artificial intelligence requires learning from given training data to recognize and predict doodles. In this paper, we leverage a diverse dataset of doodle images from individuals of various nationalities, cultures, left-handedness, and right-handedness. After training two neural networks, we determine which network offers higher accuracy and is more suitable for doodle image prediction. The motivation behind predicting doodle images using artificial intelligence lies in providing a unique perspective on human expression and intent through the utilization of neural networks. For instance, by using the various images generated by artificial intelligence based on human-drawn doodles, we expect to foster diversity in artistic expression and expand the creative domain.

• Membrane Journal
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• v.33 no.4
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• pp.158-167
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• 2023

The increasing concerns for environmental pollution and depletion of natural resources have prompted the development of environmentally sustainable technologies. Pervaporation has garnered attention in recent decades due to its low energy consumption, environmental impact, and performance efficiency. This method has been used to separate chemical species and dehydrate organic solvents, as the membranes can be fine-tuned to fulfill the desired selectivity. Several separation processes, such as reverse osmosis and distillation, are being utilized in both experimental settings and industrial applications. However, pervaporation has several advantages, such as low operating pressure and temperature and a higher rejection rate. Nonetheless, the current state of membrane technology alone can't suffice the demands of practical applications. Composite membranes, on the other hand, can leverage the benefits of both organic and inorganic materials. Many studies have effectively incorporated inorganic nanomaterials such as graphene oxide (GO) and MXene (MX) in polymeric membranes to tackle the current limitations. This review investigates the recent development of 2D composite membranes in pervaporation and evaluates performance enhancement.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.3
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• pp.121-133
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• 2023

PURPOSE: Computerization using ICF as a protocol can enhance the assessment, communication, and decision-making of various disciplines and cultures, individual functions, disabilities and health to promote communication and understanding among various professionals, organizations, and countries. The empirical foundation for these propositions was provided by delineating of six distinct computerization components. METHODS: This study analyzed 14 papers that combined the medical field and information technology to activate the ICF through computerization. From each of these papers, distinctive advantages were extracted to propose six computerization elements. The validity of these computerization elements was examined. These papers encompass various computerization elements, among which core elements were identified. In particular, six common core elements were extracted from these papers and assumed to be strategic computerization components for ICF activation. A heuristic methodology was employed to validate these components, representing IT technology maturity using four determining indices, which were then presented graphically for validation attempts. RESULTS: Four quantified indices were defined: reliability, cost-effectiveness, support and updates, and collaboration. Using these indices, this study identified elements that leverage existing IT technologies and require new development. The possibility of increasing utility was identified by applying computerization to ICF. CONCLUSION: This study examined the strategic elements of utilizing ICF by computerizing it using a protocol concept and discussed its potential for utilization. The potential to enhance the value of information in social, physical, and cultural contexts was presented by integrating various domains and data within the ICF framework.

• The Research Journal of the Costume Culture
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• v.31 no.4
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• pp.430-451
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• 2023

This study examines the effect of cosmetic certification marks on consumer behavior. The underlying objectives of this study are threefold. First, it explores whether the certification mark inserted into the cosmetic package-such as marks denoting quality assurances, ethical practices (specifically, pertaining to animal testing), and recycling packaging-affects consumer responses. Second, it investigates whether a higher number of certification marks leads to heightened positive consumer responses. Third, it analyzes the potential moderating effect of consumers' certification mark knowledge on the relationship between certification marks and consumer responses. In the pretest, certification marks with higher recognition were selected as stimuli, and a survey involving a total of 550 male and female consumers was conducted. The collected data were analyzed through ANOVA and post-hoc tests. The findings of this study confirm a significant difference in consumer responses to products based on the certification marks inserted in the cosmetic packaging. Compared to clusters without a certification mark, groups with two or more certifications (recycling certification + ethics certification, recycling certification + quality certification, recycling certification + ethics certification + quality certification) exhibit significant consumer responses. Second, more certification marks did not result in an increase in positive consumer responses. Third, a moderating effect of consumers' cosmetic certification knowledge on the certification mark-consumer response relationship was not found. The findings of this study have implications for developing product promotion strategies that leverage cosmetic certification marks as a marketing tool.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.295-307
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• 2023

The heterogeneous autoregressive (HAR) model is a simple linear model that is commonly used to explain long memory in the realized volatility. However, as realized volatility has more complicated features such as conditional heteroscedasticity, leverage effect, and volatility clustering, it is necessary to extend the simple HAR model. Therefore, to better incorporate the stylized facts, we propose a threshold HAR model with GARCH errors, namely the THAR-GARCH model. That is, the THAR-GARCH model is a nonlinear model whose coefficients vary according to a threshold value, and the conditional heteroscedasticity is explained through the GARCH errors. Model parameters are estimated using an iterative weighted least squares estimation method. Our simulation study supports the consistency of the iterative estimation method. In addition, we show that the proposed THAR-GARCH model has better forecasting power by applying to the realized volatility of major 21 stock indices around the world.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Journal of Veterinary Science
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• v.24 no.2
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• pp.20.1-20.13
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• 2023

Background: As Actinobacillus pleuropneumonniae (APP) infection causes considerable losses in the pig industry, there is a growing need to develop effective therapeutic interventions that leverage host immune defense mechanisms to combat these pathogens. Objectives: To demonstrate the role of microRNA (miR)-127 in controlling bacterial infection against APP. Moreover, to investigate a signaling pathway in macrophages that controls the production of anti-microbial peptides. Methods: Firstly, we evaluated the effect of miR-127 on APP-infected pigs by cell count/enzyme-linked immunosorbent assay (ELISA). Then the impact of miR-127 on immune cells was detected. The cytokines tumor necrosis factor (TNF)-α and interleukin (IL)-6 were evaluated by ELISA. The expression of cytokines (anti-microbial peptides [AMPs]) was assessed using quantitative polymerase chain reaction. The expression level of IL-6, TNF-α and p-P65 were analyzed by western blot. The expression of p65 in the immune cells was investigated by immunofluorescence. Results: miR-127 showed a protective effect on APP-infected macrophage. Moreover, the protective effect might depend on its regulation of macrophage bactericidal activity and the generation of IL-22, IL-17 and AMPs by targeting sphingosine-1-phosphate receptor3 (SIPR3), the element involved in the Toll-like receptor (TLR) cascades. Conclusions: Together, we identify that miR-127 is a regulator of S1PR3 and then regulates TLR/nuclear factor-κB signaling in macrophages with anti-bacterial acticity, and it might be a potential target for treating inflammatory diseases caused by APP.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.71-79
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• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.

• Korea Trade Review
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• v.42 no.2
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• pp.77-96
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• 2017

This paper attempts to analyze the influences of Korean global retailer's service marketing mix on local customers' satisfaction and their loyalty behaviors. Based on a literature review, three hypotheses are putting forward. The data from 139 customers in Weihai, China were used to test these hypotheses. This paper used Structural Equation Modeling to identify the relationship among the service marketing mix, the customer satisfaction and the customer loyalty behaviors. According to the empirical analysis, this study showed satisfactory data-fit of the proposed model and supported two of the three hypotheses. The empirical results indicated that the service marketing mix factors except the promotion factor take significant effect on the local customer satisfaction, and this in turn have influence on the customer loyalty behaviors. The result shows that Korean global retailers will need to leverage service marketing mix strategically when entering China. Practical implications of these findings needs to be considered for the global retailer to establish an effective marketing strategy.