• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.6-7
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• 2019

Asbestos has been widely used for construction materials due to its sound absorption and insulation properties. Despite the announcement that asbestos may cause cancer, asbestos demolition work has become more active. Asbestos was scattered by demolition work and the government started to regulate it. This study was started to predict the scattering asbestos concentration according to the research that it can cause cancer even if the concentration of asbestos meets legal standards. Therefore, in this paper, a regression analysis was conducted to derive a predictive equation after collecting and arranging the variables affecting scattering asbestos. As well as, artificial neural network analysis was used to make a more suitable prediction model.

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.81-86
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• 2010

Since the solar energy resource is the main input for sizing any solar photovoltaic system and solar thermal power system, it is essential to utilize the solar radiation data as a application and development of solar energy system increase. It will be necessary to understand and evaluate the insolation data. The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 and direct normal insolation data since December 1992 at 16 different locations in Korea. Because of a poor reliability of existing data, KIER's new data will be extensively used by solar energy system users as well as by research institutes. From the results, the yearly averaged horizontal global insolation was turned out 3.60kWh/$m^2$/day and a significant difference of horizontal global insolation is observed between 1982~1990 and 1991~1999, 2000~2008 through 16 different cities in Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.607-608
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• 2012

Data aggregation is a fundamental problem in wireless sensor networks which attracts great attention in recent years. Delay and energy efficiencies are two crucial issues of designing a data aggregation scheme. In this paper, we propose a distributed, energy efficient algorithm for collecting data from all sensor nodes with the minimum latency called Delay-aware Power-efficient Data Aggregation algorithm (DPDA). The DPDA algorithm minimizes the latency in data collection process by building a time efficient data aggregation network structure. It also saves sensor energy by decreasing node transmission distances. Energy is also well-balanced between sensors to achieve acceptable network lifetime. From intensive experiments, the DPDA scheme could significantly decrease the data collection latency and obtain reasonable network lifetime compared with other approaches.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.137-144
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• 2018

In this paper, we propose an well-organized lecture note for giving a better understanding on the Internet of Things (IoT) to people including non-computer majors without computing and communication knowledge. In recent years, the term 'IoT' has been popularized, and IoT will make a huge impact on our industries, societies, and environments. Although there are large amount of literature on presenting IoT from technological perspectives, few are published that are organized for teaching students having non-computer-related majors. Based on research and education experiences on IoT, we tried to make a lecture note focusing on the process of collecting data from everyday objects, transmitting and sharing data, and utilizing data to create new values for us. The proposed lecture note was employed in teaching a liberal arts class, and it was shown that students could have an understanding of what IoT really means and how IoT could change our world.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.488-491
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• 2014

Based on a complete understanding research in Information and Communication Technologies (ICT), the Intelligent Transport Systems rapidly build up innovative applications to ensure real time attainment as well remote management of driven information, provide a huge range of services and involve many actors in automotive ecosystem. In this paper, we present an intelligent cloud computing for handling data received from traffic sensing technologies. Transportations technologies applied in ITS have played a great role in collecting data from devices deployed in vehicles and highway infrastructures utilizing broadband wireless technologies to the Cloud. In order to facilitate the interested in automotive industry to use data collected and afford services to the car's owner, a scalable acquisition, access to computing resources and offered services are the primary goal of the proposed cloud computing.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.45-46
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• 2018

This paper focuses on human activities monitoring problem using onboard smartphone sensors as data generator. Monitoring such activities can be very important to detect anomalies and prevent disease from patients. Machine learning (ML) algorithms appear to be ideal approaches to use for processing data from smartphone to get sense of how to classify human activities. ML algorithms depend on quality, the quantity and even more important, the properties or features, that can be learnt from data. This paper proposes a mobile virtual reality visualization system that helps to view data representation in a very immersive way so that its quality and discriminative characteristics may be evaluated and improved. The proposed system comes as well with a handy data collecting application that can be accessed directly by the VR visualization part.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Journal of the Korean Society of Food Culture
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• v.21 no.1
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• pp.51-56
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• 2006

This research suggests critical and specific decisive variables that affect general service quality of the products in family restaurants, considering features of services in which consumption and production happens simultaneous by collecting the raw data through point of sales. It also analyses the factors and helps to offer them practical strategies by providing managers of the restaurants and marketers with empirical viewpoints based on the research. Generally, family restaurants need their own physical environment and are required to encourage customers to revisit themselves by maintaining pleasant environment as well as, considering space for customers and for employees' working routes and effective maintenance of the facilities. The result of the study also tells that even if the unimportant factors did not affect much on the restaurants, management over these factors can be a differentiated strategy for competitive advantage over the other businesses.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.45-46
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• 2023

In order to improve and promote immersive learning experiences for English as a Second Language (ESL) students, the deployment of a YOLOv5 model for object identification in videos is proposed. The procedure includes collecting annotated datasets, preparing the data, and then fine-tuning a model using the YOLOv5 framework. The study's major objective is to integrate a well-trained model into ESL instruction in order to analyze the effectiveness of AI application in the field.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.158-171
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• 2024

Diatoms, which are reported to have over 18,000 species worldwide and approximately 2,400 species in Korea, can be found in various environments including freshwater, seawater, and wetlands. They are particularly valuable for understanding global environmental changes throughout history due to their ability to maintain their shape for extended periods of time. Instead of collecting floating diatoms using nets, low-layer substrates such as gravel and leaves, as well as sedimentary surface layers, were gathered in order to identify attached diatom species. This is because attached diatoms demonstrate higher species diversity compared to floating diatoms. In this study, seven previously unrecorded diatoms were discovered in various domestic freshwater environments. Two species were found in reservoirs (Eunotia yanomami, Gomphonella pseudookunoi), two in parasitic cones (Eunotia karveerensis, Luticola minor), two in rivers (Cavinula maculata and Prestauroneis integra), and one (Surirella brebissonii var. kuetzingii) in a lagoon. The shapes, structures, and morphological characteristics of each diatom were identified using electron microscopy.