• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.118-133
• /
• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.95-111
• /
• 2021

Despite the innovation of AI and Big Data, theoretical research bout the effect of AI and Big Data on market competition is still in early stages; therefore, this paper analyzes the effect of AI, Big Data, and data sharing on an entry firm by using game theory. In detail, the firms' business environments are divided into internal and external ones. Then, AI algorithms are divided into algorithms for (1) customer marketing, (2) cost reduction without automation, and (3) cost reduction with automation. Big Data is also divided into external and internal data. this study shows that the sharing of external data does not affect the incumbent firm's algorithms for consumer marketing while lessening the entry firm's entry barrier. Improving the incumbent firm's algorithms for cost reduction (with and without automation) and external data can be an entry barrier for the entry firm. These findings can be helpful (1) to analyze the effect of AI, Big Data, and data sharing on market structure, market competition, and firm behaviors and (2) to design policy for AI and Big Data.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.139-150
• /
• 2021

Object orientation has become the predominant paradigm for conceptual modeling (e.g., UML), where the notions of class and object form the primitive building blocks of thought. Classes act as templates for objects that have attributes and methods (actions). The modeled systems are not even necessarily software systems: They can be human and artificial systems of many different kinds (e.g., teaching and learning systems). The UML class diagram is described as a central component of model-driven software development. It is the most common diagram in object-oriented models and used to model the static design view of a system. Objects both carry data and execute actions. According to some authorities in modeling, a certain degree of difficulty exists in understanding the semantics of these notions in UML class diagrams. Some researchers claim class diagrams have limited use for conceptual analysis and that they are best used for logical design. Performing conceptual analysis should not concern the ways facts are grouped into structures. Whether a fact will end up in the design as an attribute is not a conceptual issue. UML leads to drilling down into physical design details (e.g., private/public attributes, encapsulated operations, and navigating direction of an association). This paper is a venture to further the understanding of object-orientated concepts as exemplified in UML with the aim of developing a broad comprehension of conceptual modeling fundamentals. Thinging machine (TM) modeling is a new modeling language employed in such an undertaking. TM modeling interlaces structure (components) and actionality where actions infiltrate the attributes as much as the classes. Although space limitations affect some aspects of the class diagram, the concluding assessment of this study reveals the class description is a kind of shorthand for a richer sematic TM construct.

• Journal of Internet of Things and Convergence
• /
• v.6 no.4
• /
• pp.59-64
• /
• 2020

Recently, many difficulties have been faced in all parts of the world due to the impact of COVID-19. Personally, household income is decreasing sharply as many jobs disappear, and economically, many SMEs are increasingly going bankrupt. It is known that this phenomenon is highly likely to continue for the time being. In such a situation, the smart factory support project provides opportunities for difficult SMEs to improve productivity and change the corporate structure. In this study, the current status of smart factory promotion was examined, and problems occurring in the process of promoting smart factory support projects were identified. The improvement plans were derived so that more efficient projects could be promoted in the future.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.227-233
• /
• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.11
• /
• pp.1591-1600
• /
• 2021

Streptomyces coelicolor is a filamentous soil bacterium producing several kinds of antibiotics. S. coelicolor abs8752 is an abs (antibiotic synthesis deficient)-type mutation at the absR locus; it is characterized by an incapacity to produce any of the four antibiotics synthesized by its parental strain J1501. A chromosomal DNA fragment from S. coelicolor J1501, capable of complementing the abs- phenotype of the abs8752 mutant, was cloned and analyzed. DNA sequencing revealed that two complete ORFs (SCO6992 and SCO6993) were present in opposite directions in the clone. Introduction of SCO6992 in the mutant strain resulted in a remarkable increase in the production of two pigmented antibiotics, actinorhodin and undecylprodigiosin, in S. coelicolor J1501 and abs8752. However, introduction of SCO6993 did not show any significant difference compared to the control, suggesting that SCO6992 is primarily involved in stimulating the biosynthesis of antibiotics in S. coelicolor. In silico analysis of SCO6992 (359 aa, 39.5 kDa) revealed that sequences homologous to SCO6992 were all annotated as hypothetical proteins. Although a metalloprotease domain with a conserved metal-binding motif was found in SCO6992, the recombinant rSCO6992 did not show any protease activity. Instead, it showed very strong β-glucuronidase activity in an API ZYM assay and toward two artificial substrates, p-nitrophenyl-β-D-glucuronide and AS-BI-β-D-glucuronide. The binding between rSCO6992 and Zn²⁺ was confirmed by circular dichroism spectroscopy. We report for the first time that SCO6992 is a novel protein with β-glucuronidase activity, that has a distinct primary structure and physiological role from those of previously reported β-glucuronidases.

• Journal of Biomedical Engineering Research
• /
• v.43 no.1
• /
• pp.45-51
• /
• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• Journal of Microbiology and Biotechnology
• /
• v.32 no.4
• /
• pp.419-429
• /
• 2022

American ginseng (Panax quinquefolium L.) is a perennial herbaceous plant widely cultivated in China, Korea, the United States, and Japan due to its multifunctional properties. In northwest China, transplanting after 2-3 years has become the main mode of artificial cultivation of American ginseng. However, the effects of the cultivation process on the chemical properties of the soil and bacterial community remain poorly understood. Hence, in the present study, high-throughput sequencing and soil chemical analyses were applied to investigate the differences between bacterial communities and nutrition driver factors in the soil during the cultivation of American ginseng. The responses of soil nutrition in different ecological niches were also determined with the results indicating that the cultivation of American ginseng significantly increased the soluble nutrients in the soil. Moreover, the bacterial diversity fluctuated with cultivation years, and 4-year-old ginseng roots had low bacterial diversity and evenness. In the first two years of cultivation, the bacterial community was more sensitive to soil nutrition compared to the last two years. Proteobacteria, Actinobacteria, Gemmatimonadetes, Acidobacteria, Firmicutes, and Bacteroidetes dominated the bacterial community regardless of the cultivation year and ecological niche. With the increase of cultivation years, the assembly of bacterial communities changed from stochastic to deterministic processes. The high abundance of Sphingobium, Novosphingobium, and Rhizorhabdus enriched in 4-years-old ginseng roots was mainly associated with variations in the available potassium (AK), total phosphorus (TP), total potassium (TK), and organic matter (OM).

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.285-293
• /
• 2021

With the advent of the 4th Industrial Revolution, AI utilization capabilities are being emphasized in various industries, but AI education design and curriculum research as universal education is currently lacking. This study offers a design for universal AI education to further cultivate its use in universities. For the AI basic education design, a questionnaire was conducted for experts three times, and the reliability of the derived design contents was verified by reflecting the results. As a result, the main competencies for cultivating AI literacy were data literacy, AI understanding and utilization, and the main detailed areas derived were data structure understanding and processing, visualization, word cloud, public data utilization, and machine learning concept understanding and utilization. The educational design content derived through this study is expected to increase the value of competency-centered AI universal education in the future.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.4
• /
• pp.687-699
• /
• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.