• The Journal of the Korea Contents Association
• /
• v.20 no.12
• /
• pp.567-581
• /
• 2020

While Coronavirus (COVID-19) is popular all over the world, democratic citizenship is strongly highlighted as a factor that has enabled the Republic of Korea to successfully prevent it. Democratic citizenship can also be understood as a civility, which means respecting the individual's individuality, value and freedom, but at the same time pursuing common good based on healthy relationships with others in the community. It is true that despite the need for modern Christianity to practice this civility more gracefully and politely in the public sphere, some churches and Christians have failed to show it during the Corona crisis. Under these circumstances, this study made the following suggestions for the realization of communality through the practice of democratic citizenship beyond the privatization of modern Christianity. First, Christianity needs recognition as a public church and theological establishment of it. Second, modern Christianity needs to recognize the importance of a network society and practice public good more than ever. Third, modern Christianity should be able to provide a new lifestyle for the development of public character in the community. So the New Normal-era church should be able to restore its original churchlikeness by having a Christian identity and communicating gentlemanly in the public domain.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.1
• /
• pp.345-353
• /
• 2021

This study examined the effects of social capital on psychological identity and practical wellbeing for the Korean baby boom generation. To achieve this, an empirical survey was carried out on baby boomers who use elderly welfare centers and cultural centers living in D City. The overall research results showed that trust (t=6.893, p<.05), participation (t=5.157, p<.05), network (t=8.093, p<.05), and norm and reciprocity (t=4.787, p<.05), as sub-factors of social capital for baby boomers, had a significant effect on their practical wellbeing. Psychological identity was moderated (t=2.023, p<.05) in the effect of trust on practical wellbeing, adopting the hypothesis. This means that the social ties and the strong trust relationship between family members and relatives, which built up amid rapid economic growth, work with positive expectations of social capital and have a major effect on practical wellbeing. Moreover, practical welling also rated high, along with the high trust relationship and psychological identity. Consequently, various exchange programs and group and volunteer activity programs for baby boomers should be established to decrease their psychological identity due to the loss of social roles. Moreover, the decline of activities at a time of retirement can slow practical wellbeing.

• Korean journal of applied entomology
• /
• v.60 no.1
• /
• pp.1-13
• /
• 2021

The concept of integrated pest management (IPM) first developed in the 1950s, and the concept of economic control via pest management was established in the 1960s. Research on IPM began in the United States and Europe, and IPM studies in Korea started with citrus insects and paddy field pests following the distribution of high-yield varieties of rice. Apple IPM in Korea began with research on pest control using chemical pesticides and pesticides resistant to insect pests, studies on the ecology of insect pests and their natural enemies, and the exploitation of sex pheromones on insect pests. Since the 1990s, IPM research and field projects have been carried out simultaneously for farming households. In the 2000s, the development of pest monitoring and forecasting models centered on mating disturbances, database programs for pests, and networks for sharing information. IPM technology has expanded via the development of unmanned forecasting systems and automation technologies in the 2010s.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.104-112
• /
• 2021

For extrusion processes in the process industry, the need to build smart factories is increasing. However, in most extrusion production sites, the production method is continuous, and because the properties of the data are undeed, it is difficult to process the data. In order to solve this problem, we present a methodology utilizing a near field communication (NFC) sensor rather than water-based data entry. To this end, a wireless network environment was built, and a data management method was designed. A non-contact NFC method was studied for the production performance-data input method, and an analysis method was implemented using the pivot function of the Excel program. As a result, data input using NFC was automated, obtaining a quantitative effect from reducing the operator's data processing time. In addition, using the input data, we present a case where a bottleneck is improved due to quality problems.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.3
• /
• pp.91-98
• /
• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.92-104
• /
• 2021

This study aims to analyze online news articles to identify social issues related to patient safety and compare the changes in these issues before and after the implementation of the Patient Safety Act. This study performed text mining through the R program, wherein 7,600 online news articles were collected from January 1, 2010, to March 5, 2020, and examined using keyword analysis, topic modeling, and word co-occurrence network analysis. A total of 2,609 keywords were categorized into 8 topics: "medical practice", "medical personnel", "infection and facilities", "comprehensive nursing service", "medicine and medical supplies", "system development and establishment for improvement", "Patient Safety Act" and "healthcare accreditation". The study revealed that keywords such as "patient safety awareness", "infection control" and "healthcare accreditation" appeared before the implementation of the Patient Safety Act. Meanwhile, keywords such as "patient safety culture". and "administration and injection" appeared after the act's implementation with improved ranking of importance pertaining to nursing-related terminology. Interest in patient safety has increased in the medical community as well as among the public. In particular, nursing plays an important role in improving patient safety. Therefore, the recognition of patient safety as a core competency of nursing and the persistent education of the public are vital and inevitable.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.390-397
• /
• 2021

The selective public-benefit direct payment is a system that provides subsidies to farmers that improve the public benefit of agriculture. However, there are limits in improving the public benefit since the current system simply integrates the prior direct payment system. Therefore, it is necessary to improve the public benefit of agriculture by reorganizing the implementation framework. This study uses the analytic hierarchy process and analytic network process to set the priority of the system and propose an implementation framework. A survey was conducted targeting 51 experts for about two months from August 2020. Study results show that the most important goal of the system is its effectiveness. The public beneficial implementation framework of the selective public-benefit direct payment is bundle type. Meanwhile, the effects of the subcategories of the bundle type lack research. Therefore, it is necessary to conduct a pilot project for the bundled type system and systematically establish policies by analyzing the effects of the pilot project. This study provided indicators about policy directions through the evaluation of the selective public-benefit direct payment (plan). The results of this study are expected to provide an objective basis for government policies related to the reform of selective public-benefit direct payment systems in the future.

• Journal of Industrial Convergence
• /
• v.19 no.1
• /
• pp.53-63
• /
• 2021

As tourists travel to other regions, they encounter numerous facts that conflict with their views. At that time, we change our view of coping with life. In this respect, tourism is a new way of learning. As a new learning method, tourism experience research is a new approach. In this study, the relationship between experience and learning experienced in tourist destinations was analyzed by applying the activity theory. The analysis units applied in the activity theory were set as subjects, goals, communities, roles, methods and rules, outcomes, and relevance to local communities. Based on this, in-depth interviews were conducted with commentators and guides who had a great influence on the tourism experience to analyze the learning process of tourists. As a result of the analysis, the experiences of tourists during the tour were interactive in various forms within the unit as well as the unit of the activity system of the commentator and guide. This interaction induces changes in the tourism experience activity system, enabling tourists to learn. The content is that the value of learning increases as the role of guide and commentator increases, that the social and cultural dimension of tourism experience is included in the learning effect, and the contradictions that arise from interactions within or between activity systems. The fact that they find the solution process themselves, and that tourism activity is not an isolated unit, but exists at the intersection of hierarchies and networks, is affected by the activities and environments of others.

• Journal of Internet of Things and Convergence
• /
• v.6 no.4
• /
• pp.89-92
• /
• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.3
• /
• pp.71-80
• /
• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.