• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.119-126
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• 2023

Today, in order to improve fuel efficiency and dynamic behavior of automobiles, an era of light weight and simplification of automobile parts is being formed. In order to simplify and design and manufacture the shape of the product, various components are integrated. For example, in order to commercialize three products into one product, product processing is occurring to a very narrow area. In the case of existing parts, precision die casting or casting production is used for processing convenience, and the multi-piece method requires a lot of processes and reduces the precision and strength of the parts. It is very advantageous to manufacture integrally to simplify the processing air and secure the strength of the parts, but if a deep and narrow pocket part needs to be processed, it cannot be processed with the equipment's own spindle. To solve a problem, research on cutting processing is being actively conducted, and multi-axis composite processing technology not only solves this problem. It has many advantages, such as being able to cut into composite shapes that have been difficult to flexibly cut through various processes with one machine tool so far. However, the reality is that expensive equipment increases manufacturing costs and lacks engineers who can operate the machine. In the five-axis cutting processing machine, when producing products with deep and narrow sections, the cycle time increases in product production due to the indirectness of tools, and many problems occur in processing. Therefore, dedicated machine tools and multi-axis composite machines should be used. Alternatively, an angle spindle may be used as a special tool capable of multi-axis composite machining of five or more axes in a three-axis machining center. Various and continuous studies are needed in areas such as processing vibration absorption, low heat generation and operational stability, excellent dimensional stability, and strength securing by using the angle spindle.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.1-21
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• 2020

Recently, the emergence of new technologies caused by the Fourth Industrial Revolution caused a great change not only in the overall society but also in the retail industry. In the retail industry, unmanned stores based on new technologies have emerged, changing the consumption behavior of consumers. In particular, the global pandemic caused by COVID-19, which appeared in December 2019, raised social risks, and as a result of this, the beginning of the non-face-to-face era, interest in unmanned stores is increasing. In this study, the effects of benefits factors (perceived usefulness, perceived economics, perceived enjoyment, relative advantages) and sacrifice factors (perceived risk, technicality) perceived by unmanned store users on continuous use intention through perceived value. In addition, it is a study to test through empirical analysis what role the social risk from COVID-19 plays in the process of consumption through unmanned stores. The purpose of this study is to provide strategic implications for the activation of unmanned stores in the non-face-to-face era. In this study, a total of 293 copies of data were collected for users of unmanned stores for hypothesis testing. In addition, the collected data was analyzed using SPSS 21.0 and AMOS 21.0 statistical programs. The results of the study are summarized as follows. First, it was found that the perceived benefits (perceived usefulness, perceived economics, perceived playfulness, and relative advantages) of unmanned stores all had a significant positive effect on perceived value. Second, it was found that all perceived sacrifices (perceived risk, technicality) of unmanned stores had a significant negative effect on perceived value. Third, it was found that the perceived value of unmanned stores had a significant positive effect on the intention to continue use. Finally, the social risk from COVID-19 has been shown to play a moderating role when the perceived sacrifice of unmanned stores affects the perceived value.

• Land and Housing Review
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• v.14 no.4
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• pp.1-16
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• 2023

As population ageing and shrinking accompanied by dramatically expanded individual life expectancy and declining fertility rate is a global phenomenon, ageing becomes its broader perspective of ageing well embedded into sustained health and well-being, and also the fourth industrial revolution speeds up a more robust and inclusive view of smart ageing. While the latest paradigm of SA has gained considerable attention in the midst of sharply surging demand for health and social services and rapidly declining labor force, the definition has been widely and constantly discussed. This research is to constitute a conceptual framework of smart ageing (SA) from systematic literature review and the use of a series of secondary data and Geographical Information Systems(GIS), and to explore its components. The findings indicate that SA is considered to be an innovative approach to ensuring quality of life and protecting dignity, and identifies its constituents. Indeed, the construct of SA elaborates the multidimensional nature of independent living, encompassing three spheres - Aging in Place (AP), Well Aging (WA), and Active Ageing (AA). AP aims at maintaining independence and autonomy, entails safety, comfort, familiarity and emotional attachment, and it values social supports and services. WA assures physical, psycho-social and economic domains of well-being, and it concerns subjective happiness. AA focuses on both social engagement and economic participation. Moreover, the three constructs of SA are underpinned by specific elements (right to housing, income adequacy, health security, social care, and civic engagement) which are interrelated and interconnected.

• Korea Trade Review
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• v.48 no.4
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• pp.303-320
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• 2023

Recently, import and export companies have been using T/T remittance and Surrender B/L more frequently than L/C when selecting the process and method of trade payment settlement. The new trade and logistics environment is thriving in the era of the Fourth Industrial Revolution (4IR). Document-based trade transactions are undergoing a digitalization as bills of lading or smart contracts are being developed. The purpose of this study is to verify whether exporters choose export payment types based on negotiating factors. In addition, we would like to discuss the application of the characteristics of the new trade and logistics environment. Data for analysis was collected through surveys. The collection method consisted of direct visits to the company, e-mail, fax, and online surveys. The survey distribution period is from February 1, 2023, to April 30, 2023. The questionnaire was distributed in 2,000 copies, and 447 copies were collected. The final 336 copies were used for analysis, excluding 111 copies that were deemed inappropriate for the purpose of this study. The results of the study are shown below. First, among the negotiating factors, the product differentiation of exporters did not significantly affect the selection of export payment types. Second, among the negotiating factors, the greater the purchasing advantage recognized by exporters, the higher the possibility of using the post-transfer method. In addition to analyzing the results, this study suggests that exporters should consider adopting new payment methods, such as blockchain technology-based bills of lading and trade finance platforms, to adapt to the characteristics of the evolving trade and logistics environment. Therefore, exporters should continue to show interest in initiatives aimed at digitizing trade documents as a response to the challenges posed by bills of lading. In future studies, it is necessary to address the lack of social awareness in Korea by conducting advanced research abroad.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.531-538
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• 2023

Recently, many university classes have been changing from instructor-centered classes to learner-centered classes, and universities are trying to establish a new direction for university education, especially to foster talented people suitable for the Fourth Industrial Revolution. To this end, universities are presenting various competencies necessary for students and focusing on research on efficient education plans for each competency. Among them, creativity is considered the most important competency that students should obtain in universities. Developing a creative problem-solving-based subject where various majors gather to produce results while conducting creative team activities away from desk classes is considered a meaningful subject to cultivate capacities suitable for the requirements of the times. Therefore, this study purpose to develop creative problem-solving-based subjects and analyze the results of class progress. This creative problem-solving-based class is an Action Learning class for step-by-step idea development, which starts with a theoretical lecture for creative idea development and then consists of five stages of Action Learning. The tasks of action learning used in this class consisted of ceramic expression to increase the intimacy of the formed group and the group's collective expression, ideas in life to combine and compress individual ideas into one, environmental improvement programs around schools, and finally UCC on various topics. In the theoretical lecture conducted throughout the class, a class was conducted on Scientific Thinking for creative problem solving, and then a group-type action learning class was conducted sequentially. This Action Learnin process gradually increased the difficulty level and led to in-depth learning by increasing the level of difficulty step by step.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.107-124
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• 2023

As getting in 4th Industrial Revolution Times, Continuing Education Enrolled Students(CEES) trying to find loophole for jepordized current life and need job transfer have surged their interest significantly on starting new business to bring up their post career after retirement through self-improvement. Government and university have actively initiated diverse policies of promoting startup for CEES in kicking off entrepreneurship courses and programs. However, relevant main policy, 'The 2nd University Startup Education Five-Year Plan (draft)' have too chiefly focused on theoretical start-up education rather than practical courses, causing the problem of inappropriate support for implementing real startup and business (Ministry of Education, 2018). This study is brought to empirically investigate the effect of self-efficacy as perspective of the impact of entrepreneurship education on entrepreneurial intention and behavior to come up with problem of poor entrepreneurial environment and entrepreneurship education to CEES. As to empirical research, this paper deliver on-line survey to CEES from September to October 2022, collect 207 effective feedbacks, In order to verify the reliability of the scale, the Cronbach's Alpha Coefficient (Cronbach's α) was calculated, analyzed, and measured. For hypothesis test, this paper utilize the multiple regression analysis statistical analysis method and use the SPSS 22.0 statistical processing program. Empirical results show, first, it was found that self-efficacy had a significant effect on start-up education. Second, start-up education had a significant effect on the intention to start a business of adult learners. Third, start-up education had a significant effect on the start-up behavior of adult learners. Fourth, self-efficacy had a significant effect on the intention of adult learners to start a business. Fifth, self-efficacy had a significant effect on the start-up behavior of adult learners. Sixth, self-efficacy had a mediating effect in the relationship between entrepreneurship education and adult learners' intention to start a business. Seventh, self-efficacy had a complete mediating effect in the relationship between start-up education and adult learners' start-up behavior. This paper is brought three significant implications. First, main consideration developing entrepreneurship education tools for CEES need to falls on defining potential needs of CEES as segmenting as to coming up with diversity of CEES's characteristics such as gender, age, experience, education, and occupation. Second, as to design specific entrepreneurship education program, both practical training program of utilizing CEES's career field experience benchmarking best practice startup and venture cases from domestic and global, and professional startup program of CEES initiating directly startup from ideation to develop business plan with pitching and discussing. Third, entrepreneurship education for CEES should be designed to incubate self-efficacy to enhance entrepreneurial intention of implementing entrepreneurial behavior as a real, eventually leading solid support system of self-improvement for CEES' Retirement life planning.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.