• Applied Chemistry for Engineering
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• v.34 no.6
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• pp.674-678
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• 2023

The effect of Pt was investigated to the catalytic methane decomposition of CH₄ to H₂ over Pt(1)-Fe(30)/MCM-41 and Fe(30)/MCM-41 using a fixed bed flow reactor under atmosphere. The Fe₂O₃ and Pt crystal phase behavior of fresh Pt(1)-Fe(30)/MCM-41 were obtained via XRD analysis. SEM, EDS analysis, and mapping were performed to show the uniformed distribution of nano particles such as Fe, Pt, Si, O on the catalyst surface. XPS results showed O^2-, O^- species and metal ions such as Pt⁰, Pt²⁺, Pt⁴⁺, Ft⁰, Fe²⁺, Fe³⁺ etc. When 1 wt% of Pt was added to Fe(30)/MCM-41, automic percentage of Fe2p increased from 13.39% to 16.14%, and Pt4f was 1.51%. The yield of hydrogen over Pt(1)-Fe(30)/MCM-41 was 3.2 times higher than Fe(30)/MCM-41. The spillover effect of H₂ from Pt to Fe increased the reduction of Fe particles and moderate interaction of Fe, Pt and MCM-41 increased the uniform dispersion of fine nanoparticles on the catalyst surface, and improved hydrogen yield.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.574-593
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• 2023

In this study, the effectiveness and applicability of a newly designed Compact CNS shear box for conducting direct shear tests on jointed rock specimens were investigated. CNS joint shear tests were conducted on jointed rocks with Artificially generated roughness while varying the fracture surface roughness coefficient and initial normal stress conditions. In addition, displacement data were validated by Digital image correlation analysis, fracture patterns were observed, and comparative analysis was conducted with previously studied shear behavior prediction models. Furthermore, the accuracy of the displacement data was confirmed through DIC analysis, the fracture patterns were observed, and the shear properties obtained from the tests were compared with existing models that predict shear behavior. The findings exhibited a strong correlation with specific established empirical models for predicting shear behavior. Furthermore, the potential linkage between the characteristics of shear behavior and fracture patterns was deliberated. In conclusion, the CNS shear box was shown to be applicable and effective in providing data on the shear characteristics of the joint.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.79-92
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• 2023

With a growing interest in the Han River Skyline, which greatly influences Seoul's image, careful consideration of the skyline form has become crucial in the redevelopment plans for apartment complexes along the Han River. The Seoul Metropolitan City government has lifted the height limitations for apartments along the Hang River to cultivate a vibrant skyline. However, traditional skyline analysis often overlooks specific attributes, limiting the provision of precise guidelines for Seoul's unique skyline plans. Despite advancements in Digital Twin technology, only some tools effectively manage urban skylines with preferred shapes. Hence, this study aims to make a substantial contribution to the advancement of a Digital Twin 3D modeling program capable of effectively managing urban skylines. This is achieved through the utilisation of Conjoint Analysis, which assesses the importance of each attribute in determining the preferred skyline shape. Focusing on Apgujeong apartment complexes along the Han River currently undergoing redevelopment or planned for redevelopment, the study analyses the preferred skyline shape to propose standards for the Digital Twin 3D modeling program development. It also suggests that Conjoint Analysis can be beneficial in this process.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.223-235
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• 2022

The satisfaction and performance levels of employees also depend upon the leadership styles adopted by corporate leaders. Meanwhile, organizational identification is a fundamental organizational behavioural concept in business that influences employee belongingness with the organization. Taking into account the relevance of this research topic, this paper aims to understand of organizational identification and leadership styles in SMEs. It also investigated the mediation role of organizational true. For the analysis, a questionnaire survey was conducted on employees of SMEs, and the collected data were analyzed using the hierarchical multiple regression analysis. Analysis results are as follows: First, out of leadership style, transformational leadership, transactional Leadership had a positive effect on job satisfaction. Second, organizational identification had a positive effect on job satisfaction. Third, in the relationship between leadership, organizational identification and job satisfaction, the mediating effect of organizational true had significant transformational leadership→organizational truer→job satisfaction, transactional leadership→organizational truer→job satisfaction, organizational identification→organizational truer→job satisfaction. The results of this study show that the role of middle managers is very important in SMEs. The leadership of SME managers can also be linked to organizational performance through the job satisfaction of workers. Therefore, SME' CEO should provide opportunities to receive professional training on the leadership of middle managers. In addition, SME' CEO need a strategy to instill a positive image of the organization in workers through the organizational vision.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Information Systems Review
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• v.23 no.3
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• pp.25-50
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• 2021

Though there have been a lot of studies about online product review, the effects of highlighted reviewhave not been examined enough. Highlighted review is a type of review that the platform designer changes its size or position in order to highlight without any sponsorship or incentive. The main subject of this study is about how highlighted review type affects consumer's perception and behavior in online information acquisition. We collected data from 171 subjects to test hypotheses. Using three different types of screen captures, we compared three groups - general review group, positive highlighted review only group, and both positive and negative highlighted review group. As a result, disclosing both of positiveand negative highlighted review was perceived more useful than disclosing only positive highlighted review. However, correlation between highlighted review type and review skepticism was not statistically significant. The impacts of review usefulness and skepticism on platform credibility were statistically significant, and the correlation between platform credibility and usage intention was also significant. All of results is almost similar across two product types, search goods and experiential goods. This research provides practical implications to online shopping platform designers when they design review systems to make people use their platforms.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• Research in Plant Disease
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• v.29 no.4
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• pp.405-413
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• 2023

Bacterial blight of carrot caused by Xanthomonas hortorum pv. carotae (Xhc) is one of the serious diseases of carrot, of which control measures has not been still established in the domestic farm. In this study, in order to select effective sterilizer for bacterial blight of carrots, three antibiotics such as streptomycin, oxolinic acid, kasugamycin, two copper compounds like copper hydroxide and copper sulfate basic and three rhizobacteria Burkholderia gladioli MRL408-3, Pseudomonas fluorescens TRH415-2 and Bacillus cereus KRY505-3 were selected to investigate their direct antibacterial effects using artificial media, aiming to identify effective pesticides against Xhc. Among them, treated medium with antibiotics such as streptomycin, oxolinic acid, and the antagonistic rhizobacteria MRL408-3 were formed inhibition zone. The agrochemicals and the rhizobacteria MRL408-3, which showed antibacterial effects on carrot leaves, pre-treated on the carrot leaves and then inoculated with Xhc. High control effects were shown on the carrot leaves pre-treated with both streptomycin and oxolinic acid. Scanning electron microscopy images of the carrot leaf surfaces showed that the population of bacteria decreased significantly on leaves pre-treated with streptomycin and oxolinic acid. From these results, it can be inferred that antibiotics like streptomycin and oxolinic acid exhibit superior control effects compared to other agents. This study provides valuable insights towards establishing an effective control system for bacterial blight of carrot.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data