• Women's Health Nursing
• /
• v.10 no.1
• /
• pp.32-41
• /
• 2004

Purpose: The purpose of this study was to explore and describe the process of life adaptation in women with cervical cancer. Method: A grounded theory method with guided data collection and analysis was used. Fifteen women with cervical cancer who had some kind of treatment at the hospital were asked open-ended and descriptive questions with a guideline. All interviews were tape-recorded and transcribed verbatim. Result: The core category that emerged from the comparative analysis was "overcoming cancer" named as a process of life adaptation in the participants. The process of overcoming cancer evolved three stages - "admitting reality", "attempting health-care" and "continuing health-care". Depending on the paradigm model, the central phenomena of the experiences was "powerlessness". The internal factors motivating women to overcome cancer were "denial of cancer" and "desire for life". Strategies of overcoming cancer included "living as a cancer patient," "selective food eating," "steady exercising," "getting rid of stress," and "preparing for death". The intervening conditions, "supportive system," "forms of life," and "burden on family," influenced overcoming cancer. Conclusion: The results of this study could help clinical nurses to understand life adaptation in cervical cancer patients and establish efficient coping strategies in dealing with the problems they face.

• Journal of The Korean Association of Information Education
• /
• v.23 no.6
• /
• pp.639-653
• /
• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.1
• /
• pp.131-140
• /
• 2013

Due to the limitations of the existing indoor positioning system depending on the radio wave, at present, it is required to introduce a new method in order to improve the accuracy in indoor environment. Recently, bio-inspired technology has become the future core technology. Thus, this study examined the accurate positioning method applying the abilities that animals with homing instinct measure their position by searching geomagnetic field with the use of their biomagnets. In order to confirm the applicability of geomagnetic field, a new source for indoor positioning, this study separated the constituent materials and building structure and designed the structures that can carry the actual magnetic field sensor and the data collection module. Subsequently, this study investigated the applicability of geomagnetic field as a positioning source by establishing the positioning system of Fingerprint method. In performance evaluation of the positioning system, the geomagnetic strength-based positioning system was similar to or approximately 20 percent higher than the wireless LAN-based positioning system in the buildings with the existing wireless LAN. Thus, in the environment without infrastructure for indoor positioning, the geomagnetic, an independent earth resource, can make it possible to realize the indoor positioning.

• The KIPS Transactions:PartA
• /
• v.13A no.4 s.101
• /
• pp.295-304
• /
• 2006

Java has been established as one of the most widely-used languages owing to its support of object-oriented concepts, safety, and flexibility. Garbage collection in the Java virtual machine is a core component that relieves application programmers of difficulties related to memory management. In this paper, we propose a memory manager and a garbage collector that is implemented on a embedded java virtual machine. The memory manager divide a heap into various-sized cells and manages it as blocks of same-sized cells. So it is possible to allocate and free memory fast. We adopted the 3-color based Mark & Sweep garbage collector as our base algorithm and we propose 4-color based Mark & Sweep garbage collector for supporting multi-threaded program. The proposed garbage collector occurs memory fragmentation but we show through a experiment that the fragmentation ratio is almost fixed according as we create objects continuously.

• Smart Structures and Systems
• /
• v.6 no.5_6
• /
• pp.595-618
• /
• 2010

This paper describes the application of a wireless sensor network to a 31 meter-tall medieval tower located in the city of Trento, Italy. The effort is motivated by preservation of the integrity of a set of frescoes decorating the room on the second floor, representing one of most important International Gothic artworks in Europe. The specific application demanded development of customized hardware and software. The wireless module selected as the core platform allows reliable wireless communication at low cost with a long service life. Sensors include accelerometers, deformation gauges, and thermometers. A multi-hop data collection protocol was applied in the software to improve the system's flexibility and scalability. The system has been operating since September 2008, and in recent months the data loss ratio was estimated as less than 0.01%. The data acquired so far are in agreement with the prediction resulting a priori from the 3-dimensional FEM. Based on these data a Bayesian updating procedure is employed to real-time estimate the probability of abnormal condition states. This first period of operation demonstrated the stability and reliability of the system, and its ability to recognize any possible occurrence of abnormal conditions that could jeopardize the integrity of the frescos.

• Journal of Convergence for Information Technology
• /
• v.11 no.9
• /
• pp.138-144
• /
• 2021

In the electronic component assembly business, when product defects occur, it is important to track incoming raw material defects or work defects, and it is important to improve suppliers or work sites according to the results. The core task of the smart factory is to build an integrated data hub to process storage, management, and analysis in real time, and to manage cluster processes, energy, environment, and safety. In order to improve reliability through accurate analysis and collection of production data by real-time monitoring of production site management for electronic parts-related small and medium-sized enterprises (SMEs), the establishment of a smart factory is essential. This paper was developed to be utilized in the construction by defining the system configuration method, smart factory-related technology and application cases, considering the characteristics of SMEs related to electronic components that want to introduce a smart factory.

• The Plant Pathology Journal
• /
• v.35 no.4
• /
• pp.321-329
• /
• 2019

Ascochyta blight caused by Ascochyta rabiei (Pass.) Lab. (Telomorph: Didymella rabiei) (Kov.) is one of the most important fungal diseases in chickpea worldwide. Knowledge about pathogen aggressiveness and identification resistance sources to different pathotypes is very useful for proper decisions in breeding programs. In this study, virulence of 32 A. rabiei isolates from different part of Iran were analyzed on seven chickpea differentials and grouped into six races based on 0-9 rating scale and susceptibility/resistant pattern of chickpea differentials. The least and most frequent races were race V and race I, respectively. Race V and VI showed highly virulence on most of differential, while race I showed least aggressiveness. Resistance pattern of 165 chickpea genotypes also were tested against six different A. rabiei races. ANOVA analysis showed high significant difference for isolate, chickpea genotypes and their interactions. Overall $chickpea{\times}isolate$ (race) interactions, 259 resistance responses (disease severity ${\leq}4$) were identified. Resistance spectra of chickpea genotypes showed more resistance rate to race I (49.70%) and race III (35.15%), while there were no resistance genotypes to race VI. Cluster analysis based on disease severity rate, grouped chickpea genotypes into four distinct clusters. Interactions between isolates or races used in this study, showed the lack of a genotype with complete resistance. Our finding for virulence pattern of A. rabiei and newly identified resistance sources could be considerably important for integration of ascochyta blight resistance genes into chickpea breeding programs and proper decision in future for germplasm conservation and diseases management.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.6
• /
• pp.684-690
• /
• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• Journal of Korean Society on Water Environment
• /
• v.36 no.6
• /
• pp.611-620
• /
• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.179-185
• /
• 2020

Recently, in order to improve the performance of the Earthquake Early Warning System (EEWS) and to supplement the effects of earthquake disaster prevention in epicenters or near epicenters, development of on-site EEWS has been attempted. Unlike the national EEWS, which is used for earthquake disaster prevention by using seismic observation networks for earthquake research and observation, on-site EEWS aims at earthquake disaster prevention and therefore requires efficient design and evaluation in terms of performance and cost. At present, Korea lacks the necessary core technologies and operational know-how, including the use of existing EEWS design criteria and evaluation methods for the development of On-Site EEWS as well as EEWS. This study proposes hardware and software design directions and performance evaluation items and methods for seismic data collection, data processing, and analysis for localization of On-Site EEWS based on the seismic accelerometer requirements of the Seismic and Volcanic Disaster Response Act.