• Journal of Platform Technology
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• v.11 no.5
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• pp.97-103
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• 2023

Unlike GPS, which is an outdoor positioning technology that is universally and uniformly used all over the world, various technologies are still being developed in the field of indoor positioning technology. In order to acquire accurate indoor location information, a standard of representative indoor positioning technology is required. Recently, indoor positioning technology is expanding into the Real Time Location Service (RTLS) area based on high-precision location data. Accordingly, a new type of indoor positioning technology is being proposed. Thanks to the development of artificial intelligence, artificial intelligence-based indoor positioning technology using wireless signal data of a smartphone is rapidly developing. At this time, in the process of collecting data necessary for artificial intelligence learning, data that is distorted or inappropriate for learning may be included, resulting in lower indoor positioning accuracy. In this study, we propose a data preprocessing technology for artificial intelligence learning to obtain improved indoor positioning results through the refinement process of the collected data.

• Journal of Science Education
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• v.48 no.2
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• pp.101-110
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• 2024

This study compares two analogical models employed in the instruction of cosmic expansion to assess their impact on the comprehension of middle and high school students. Among the most frequently used models, the balloon model, in which the surface of a balloon represents the Universe, and the bread model, in which the bread itself symbolizes the Universe, were chosen. Using the balloon model, students are required to conceptualize the 2-dimensional surface as representing a higher-dimensional space. Using the bread model, students need to visualize the Universe as the interior of the bread prior to slicing it. For middle school students who had not yet studied cosmic expansion, the balloon model proved more effective in conveying the fundamental scientific concept that the expanding Universe has no center. High school students, who were already familiar with the concept of expansion without a center from previous lessons, found it easier to map the analogy onto the target using the bread model. Based on these results, we conclude that employing multiple models is necessary to complement any single analogy, given its inherent limitations.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Environmental Policy
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• v.11 no.2
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• pp.3-16
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• 2012

Objectives of the Korean Environmental Conservation Value Assessment Map (ECVAM) is to evaluate environmental value used in comprehensive environmental information in order to encourage eco-friendly land use and management. The first research was conducted in 2001 to establish the evaluation items and the criteria of the ECVAM, and the first nationwide map was established in the period of 2003 to 2005. The maps are updated annually to reflect environmental changes of land. The evaluation items and the criteria have been modified based on feasibility studies to improve the accuracy of the maps. This study re-evaluated the ECVAMs from 2005 to 2010 with criteria used in current environment and analyzed the changes in the area of the maps in 6 years. This is also an investigation on the maps whether they are appropriate as an index for sustainable environmental monitoring. The result shows that the 1st grade level of the ECVAM area with the highest conservation value had been expanding since 2005. These changes were analyzed in terms of updating the 4th Forest Map (2008) produced once every 10 years, reflecting the new legal protected areas such as Baekdudaegan Protected Area(2010), and the environmental/ecological assessment items such as the National Ecological Network (2009). This mean the ECVAM are a monitoring index that integrates individual environmental indexes including the increase of forest age and diameter due to sustainable management of forest areas, and the change of conservation areas. Therefore, ECVAM can be used as a new index integrating national environmental indicators for monitoring changes of national environment and policy. In order to utilize the ECVAM, improving accuracy and reducing renewal cycle time of thematic maps are required.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.981-986
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• 2020

The Internet of Things refers to a space-of-things connection network configured to allow things with built-in sensors and communication functions to interact with people and other things, regardless of the restriction of place or time.IoT is a network developed for the purpose of services for human convenience, but the scope of its use is expanding across industries such as power transmission, energy management, and factory automation. However, the communication protocol of IoT, MQTT, is a lightweight message transmission protocol based on the push technology and has a security vulnerability, and this suggests that there are risks such as personal information infringement or industrial information leakage. To solve this problem, we designed a synchronous MQTT security channel that creates a secure channel by using the characteristic that different chaotic dynamical systems are synchronized with arbitrary values in the lightweight message transmission MQTT protocol. The communication channel we designed is a method of transmitting information to the noise channel by using characteristics such as random number similarity of chaotic signals, sensitivity to initial value, and reproducibility of signals. The encryption method synchronized with the proposed key value is a method optimized for the lightweight message transmission protocol, and if applied to the MQTT of IoT, it is believed to be effective in creating a secure channel.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.56-74
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• 2010

The overseas expansion of global logistics company (GLC) is increasing rapidly under the influence of international specialization of manufacturing, and the necessity of global logistics service is increasing, and then the logistics market is growing year to year. The purposes of this study are to investigate the growing factors and strategies making global logistics company and to suggest the best strategies for overseas expansion of domestic logistics company (DLC) by using imitation strategy. The major results are as follows ; Firstly, DLC has to imitate liner shipping company or terminal operator which has competitiveness rather than other part of logistics. Secondly, DLC has to build up competition through investigation the global logistics companies which have globalization, specialization, monopolization and public elements. Thirdly, DLC has to use 'coming from behind strategy' for the exiting market, the 'pioneer importer strategy' for the new emerging market or niche. Lastly, DLC has to make a road map or process for expanding the logistics service area without collision exiting business models.

• Spatial Information Research
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• v.21 no.3
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• pp.89-101
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• 2013

Recently, importance of indoor space is on the rise, as larger and more complex buildings are taking place due to development of building technology. Accordingly, range of the target area of spatial information service is rapidly expanding from outdoor space to indoor space. Various demands for indoor spatial information are expected to be created in the future through development of high technologies such as IT Mobile and convergence with various area. Thus this research takes a look at available methods for building indoor spatial information and then builds high accuracy three-dimensional indoor spatial information using indoor high accuracy laser survey and 3D vector process technique. The accuracy of built 3D indoor model is evaluated by overlap analysis method refer to a digital map, and the result showed that it could guarantee its positional accuracy within 0.04m on the x-axis, 0.06m on the y-axis. This result could be used as a fundamental data for building indoor spatial data and for integrated use of indoor and outdoor spatial information.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.2 s.40
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• pp.59-66
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• 2007

The digital working environment and its related technology have been rapidly expanding. In the surveying field, we have changed from using optical film cameras and plotters to digital cameras, multi sensors like GPS/INS etc,. The old analog work flow is replaced by a new digital work flow. Accurate data of the land is used in various fields, efficient utilization and management of land, urban planning, disaster and environment management. It is important because it is an essential infrastructure. For this study, LiDAR surveying was used to get points clouds in the study area. It has a high vegetation penetrating advantage and we used a digital process from planning to the final products. Contour lines were made from LiDAR data and compared with national digital base maps (scale 1/1,000 and 1/5,000). As a result, the accuracy and the economical efficiency were evaluated. The accuracy of LiDAR contour data was average $0.089m{\pm}0.062\;m$ and showed high ground detail in complex areas. Compared with 1/1,000 scale contour line production when surveying an area over $100\;km^2$, approximately 48% of the cost was reduced. Therefore we prepose LiDAR surveying as an alternative to modify and update national base maps.

• Proceedings of the Microbiological Society of Korea Conference
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• 2007.05a
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• pp.120-122
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• 2007

All living organisms use numerous signal-transduction pathways to sense and respond to their environments and thereby survive and proliferate in a range of biological niches. Molecular dissection of these signalling networks has increased our understanding of these communication processes and provides a platform for therapeutic intervention when these pathways malfunction in disease states, including infection. Owing to the expanding availability of sequenced genomes, a wealth of genetic and molecular tools and the conservation of signalling networks, members of the fungal kingdom serve as excellent model systems for more complex, multicellular organisms. Here, we employed Cryptococcus neoformans as a model system to understand how fungal-signalling circuits operate at the molecular level to sense and respond to a plethora of environmental stresses, including osmoticshock, UV, high temperature, oxidative stress and toxic drugs/metabolites. The stress-activated p38/Hog1 MAPK pathway is structurally conserved in many organisms as diverse as yeast and mammals, but its regulation is uniquely specialized in a majority of clinical Cryptococcus neoformans serotype A and D strains to control differentiation and virulence factor regulation. C. neoformans Hog1 MAPK is controlled by Pbs2 MAPK kinase (MAPKK). The Pbs2-Hog1 MAPK cascade is controlled by the fungal "two-component" system that is composed of a response regulator, Ssk1, and multiple sensor kinases, including two-component.like (Tco) 1 and Tco2. Tco1 and Tco2 play shared and distinct roles in stress responses and drug sensitivity through the Hog1 MAPK system. Furthermore, each sensor kinase mediates unique cellular functions for virulence and morphological differentiation. We also identified and characterized the Ssk2 MAPKKK upstream of the MAPKK Pbs2 and the MAPK Hog1 in C. neoformans. The SSK2 gene was identified as a potential component responsible for differential Hog1 regulation between the serotype D sibling f1 strains B3501 and B3502 through comparative analysis of their meiotic map with the meiotic segregation of Hog1-dependent sensitivity to the fungicide fludioxonil. Ssk2 is the only polymorphic component in the Hog1 MAPK module, including two coding sequence changes between the SSK2 alleles in B3501 and B3502 strains. To further support this finding, the SSK2 allele exchange completely swapped Hog1-related phenotypes between B3501 and B3502 strains. In the serotype A strain H99, disruption of the SSK2 gene dramatically enhanced capsule biosynthesis and mating efficiency, similar to pbs2 and hog1 mutations. Furthermore, ssk2, pbs2, and hog1 mutants are all hypersensitive to a variety of stresses and completely resistant to fludioxonil. Taken together, these findings indicate that Ssk2 is the critical interface protein connecting the two-component system and the Pbs2-Hog1 pathway in C. neoformans.