• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.2
• /
• pp.223-230
• /
• 2019

The study aims to contribute to the improvement of project management in Vietnam. It focuses on developing new critical success factors (CSFs) which can be used to assess the success of project management in the country. This is a promising issue considering the rapid changes occurring within the business environment. The reason is because CSFs carry great consequences on project management issues, particularly in the context of Vietnam, which is currently experiencing many big scale projects involving both local and foreign investors. Two applications are utilised. One is to adapt the business model of Belassi and Tukel (1996) to observe the transitional and emerging economy of Vietnam. The other is to examine the data collected from a survey to examine the new CSFs which can then be used to assess the success of its projects and project management in Vietnam. The research results showed some remarkable differences between CSFs of Vietnam and foreign countries in both number of success factors and its impact levels which should be paid attention by foreign project managers/owners when doing investment and project management in Vietnam. The outcome generated can be useful to project owners/managers as well as policy makers in Vietnam's business environment.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1243-1248
• /
• 2018

Metal Powder Metallurgy is a manufacturing technology that makes unique model parts or a certain type of product by using a hardening phenomenon when a powder of metal or metal oxide is put it into a mold and compression-molded by a press and then heated and sintered at a high temperature. Powder metallurgical press equipment is mainly used to make the parts of automobile, electronic parts and so on, and most of them are manufactured using precise servo motor. The intelligent precision servo control powder molding press system which is designed and implemented in this paper has advantages of lowering the price and maintaining the precision by using the mechanical camshaft for the upper ram part and precisely controlling the lower ram part using the high precision servo system. In addition, OPC-based monitoring and process data collection systems are designed and implemented to provide scalability that can be applied to smart manufacturing management systems that utilize Big Data in the future.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.941-947
• /
• 2018

Station transfers for smart card based railway networks refer to transfer pedestrian movements that occur at the origin and destination nodes rather than at a middle station. To calculate the optimum path for the railway network, a penalty for transfer pedestrian movement must be included in addition to the cost of within-car transit time. However, the existing link label-based path searching method is constructed so that the station transfer penalty between two links is detected. As such, station transfer penalties that appear at the origin and destination stations are not adequately reflected, limiting the effectiveness of the model. A ghost node may be introduced to expand the network, to make up for the station transfer penalty, but has a pitfall in that the link label-based path algorithm will not hold up effectively. This research proposes an optimal path search algorithm to reflect station transfer penalties without resorting to enlargement of the existing network. To achieve this, a method for applying a directline transfer penalty by comparing Ticket Gate ID and the line of the link is proposed.

• Journal of Industrial Convergence
• /
• v.20 no.6
• /
• pp.7-15
• /
• 2022

Recently, due to the development of AI and big data technologies following the advent of the era of the 4th Industrial Revolution, the emerging metaverse industry is emerging as a new business, and in particular, from this point of view, this paper analyzes the history of metaverse and the pros and cons of "Geppetto", which is the most popular in the Korean metaverse market, and aims to give an appropriate direction for future development based on this. In order to carry out this study, we first used SWOT analysis techniques as an initial enterprise analysis method to examine the strengths and weaknesses, opportunities and threat requirements, and derive the status of each factor. Based on the factors in each of the subsequent derivatives, we wanted to explore the TOWS development strategy and present significant implications based on this.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.6
• /
• pp.843-852
• /
• 2022

Despite continuous safety management, the death rate of construction workers is not decreasing every year. Accordingly, various studies are in progress to prevent construction site accidents. In this paper, we developed an AI-based priority inspection target selection model that preferentially selects sites are expected to cause construction accidents among construction sites with construction costs of less than 5 billion won (KRW). In particular, Random Forest (90.48 % of accident prediction AUC-ROC) showed the best performance among applied AI algorithms (Classification analysis). The main factors causing construction accidents were construction costs, total number of construction days and the number of construction performance evaluations. In this study an ROI (return of investment) of about 917.7 % can be predicted over 8 years as a result of better efficiency of manual inspections human resource and a preemptive response to construction accidents.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1055-1065
• /
• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.99-105
• /
• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Korean Journal of Heritage: History & Science
• /
• v.57 no.1
• /
• pp.6-19
• /
• 2024

The Korean Archaeological Dictionary, which represents Korean archaeological knowledge information, contains refined and high-quality information written by expert collective intelligence. This is a characteristic that clearly distinguishes it from overseas archaeological data archives, and can be called differentiated infrastructure data. However, it has not played a role as an information service or knowledge information platform reflecting the latest digital technology. As a way to maximize these strengths and compensate for weaknesses, it was proposed to develop and operate a GIS-based knowledge and information platform for Korean archaeology. To realize this, it is necessary to develop a title management system centered on repositories and metadata that can collect and store various information, link open linked data design and related systems, develop a search function that can analyze and visualize data in response to the big data era, and establish a WebGIS-based information service system. This will be a platform to continuously manage, supplement, and update Korean archaeological knowledge information, build a ubiquitous environment where anyone can use information anytime, anywhere, and create various types of business models.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.35 no.3
• /
• pp.137-159
• /
• 2024

This study analyzed the number of electronic journal subscriptions, subscription fees, usage, cost-effectiveness, etc. of university libraries to identify limitations and problems. Based on this, a case library was selected and the simulation tools Unsub was used to compare the efficiency of package subscriptions and individual subscriptions and suggest improvement measures. In the case of C University Library, SAGE and Emerald were found to be cost-effective as package subscriptions, while ScienceDirect and OUP were found to be worth considering for individual subscriptions. In particular, OUP was found to save £6,063 per year with individual subscriptions, and the ratio of accessible and usable articles reached 85.8%. In addition, ScienceDirect was found to be the most effective model for combining individual subscriptions and pay-per-view systems, considering budget savings and ease of use. Based on the results of this case study, the limitations of Unsub's application and policy tasks for developing a 'K-Unsub' version suitable for the Korean situation were suggested.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1427-1435
• /
• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.