• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.310-318
• /
• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.4
• /
• pp.44-57
• /
• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.629-643
• /
• 2019

In the era of the 4th industrial revolution, countries around the world are conducting projects to rapidly expand smart factory to secure competitiveness in manufacturing industries. However, unlike existing factories where the network environment was closed, smart factories can be vulnerable because internal and external objects are interconnected and various ICT technologies are used. And smart factories are likely to be the subject of cyber-attacks that are designed to cause monetary damage to certain targets because economic damage is so serious when an accident occurs. Therefore, it is necessary to study and apply security for smart factories, but there is no specific smart factory system architecture, so there is no establish for smart factory security requirements. In order to solve these problems, this paper derives the smart factory architecture that can extract and reflect the main characteristics of a smart factory based on the domestic and foreign reference model of smart factories. And this paper identifies the security threats based on the derived smart factory architecture and present the security requirements to cope with them for contributing to the improvement of the security of the smart factory.

• Journal of The Korean Astronomical Society
• /
• v.52 no.1
• /
• pp.23-30
• /
• 2019

The KVN(Korean VLBI Network)-style simultaneous multi-frequency receiving mode is demonstrated to be promising for mm-VLBI observations. Recently, other Very long baseline interferometry (VLBI) facilities all over the globe start to implement compatible optics systems. Simultaneous dual/multi-frequency VLBI observations at mm wavelengths with international baselines are thus possible. In this paper, we present the results from the first successful simultaneous 22/43 GHz dual-frequency observation with KaVA(KVN and VERA array), including images and astrometric results. Our analysis shows that the newly implemented simultaneous receiving system has brought a significant extension of the coherence time of the 43 GHz visibility phases along the international baselines. The astrometric results obtained with KaVA are consistent with those obtained with the independent analysis of the KVN data. Our results thus confirm the good performance of the simultaneous receiving systems for the nonKVN stations. Future simultaneous observations with more global stations bring even higher sensitivity and micro-arcsecond level astrometric measurements of the targets.

• Journal of Climate Change Research
• /
• v.8 no.4
• /
• pp.425-442
• /
• 2017

This study analysed the linkage between national forest policy in Korea, namely the $5^{th}$ National Forest Master Plan, 2016 Korea Forest Service Performance Management Plan, the $3^{rd}$ National Sustainable Development Plan, and UN Sustainable Development Goals (SDGs). The 7 strategies of the $5^{th}$ National Forest Master Plan were related to 11 Goals of SDGs, and 5 strategies of 2016 Korea Forest Service Performance Management Plan were associated with 7 areas of SDGs, and 4 strategies within $3^{rd}$ National Sustainable Development Plan were linked to 7 Goals of SDGs. Among 87 national forest indicators compiled from three respective forest-related policies of Korea, 45 national indicators were related to 18 SDGs indicators. This indicates that 52% of national indicators of Korean forest policy are reflecting the language of SDGs. However, seeing from SDGs perspective, only 18 out of 241, which accounts for 7.8% of SDGs indicators are related to national indicators. The findings imply that a number of national forest-related indicators do not meet the diverse dimension of SDGs which provides potential areas for forest to contribute. Based on the findings, following recommendations were suggested: 1) the term used in forest policy should be aligned to SDGs targets so that it can be embedded in national policies, and 2) indicators should be further contextualized as well as in its assessment system. Lastly, it suggests for leveraging 3) '5 Processes of sub-national climate change adaptation plan' and the core concept of REDD+ MRV which could provide fundamental background for implementing SDGs framework to national forest policy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.168-175
• /
• 2019

The purpose of this paper is to examine the characteristics of organic relations according to the characteristics of space and place in which human activities are spreading. The diffusion of various human experiences and the interaction of space and place that form a mutual construction network create momentum for expanding individual or collective experiences. The space and place that lead to the interactions of human activities are expressed in various ways. Through the domestic travel program especially, the ways of expressing space and place are spreading as various "fields of experience." The composition and the expression system of a travel program is transformed into a subject of thought, not a mere experience of space and place through the formation of a "field of experience" that can be thought out and thought of as an existing travel style. Therefore, in this study, we examine the characteristics of the MBC Plus channel from a socio-cultural perspective, expressed through the experience of space and place characteristics expressed through a travel program. The result of an analysis of story development composition for this study shows that the experience of space and place minimizes the boundaries of various cultural discounts according to their relationships to food and communication. In addition, the analysis results of a three-way communication structure show that the emotional code of the audience is maximized through the process of maximizing the effect of reality, and minimizing the gap between the cultural differences and the boundaries through the empathic situation and the empathic subject.

• Korean journal of applied entomology
• /
• v.58 no.1
• /
• pp.9-13
• /
• 2019

Nematode-trapping fungi use various specialized traps to capture nematodes. A fungus that can capture nematodes in three dimensional adhesive networks was isolated from the soil around the root of Cucumis melo L. (Oriental melon) in Seongju, Korea. The conidiophores were found to be septate, hyaline, erect and $290-528(342.8){\mu}m$ high. It produces obovoid shape and 1-3 septate (commonly 2-septate) conidia with a size of $30.5{\times}20.3{\mu}m$. Molecular analysis of 5.8 S rDNA displayed 99% similarity to Arthrobotrys sinensis. On the basis of morphological, morphometric and molecular studies, the fungus was identified as A. sinensis. It is the first report in Korea which can be one of biological control resource of plant-parasitic nematode.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.155-170
• /
• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of IKEEE
• /
• v.23 no.3
• /
• pp.1096-1099
• /
• 2019

In this paper, we propose the protocol for construction of smart factory. The proposed protocol for construction of smart factory consists of an OPC UA Server/Client, a technology of TSN realtime communication, a NTP & PTP time synchronization protocol, a FieldBus protocol and conversion module, a technology of saving data for data transmit latency and synchronization protocol. OPC UA server/client is a system integration protocol which makes interface industrial hardware device and supports standardization which allows in all around area and also in not independent from any platform. A technology of TSN realtime communication provides an high sensitive time management and control technology in a way of sharing specific time between devices in the field of high speed network. NTP & PTP time synchronization protocol supports IEEE1588 standardization. A fieldbus protocol and conversion module provide an extendable connectivity by converting industrial protocol to OPC. A technology of saving data for data transmit latency and synchronization protocol provide a resolution function for a loss and latency of data. Results from testing agencies to assess the performance of proposed protocol for construction of smart factory, response time was 0.1367ms, synchronization time was 0.404ms, quantity of concurrent access was 100ea, quantity of interacting protocol was 5ea, data saving and synchronization was 1,000 nodes. It produced the same result as the world's highest level.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.341-348
• /
• 2019

In this paper, a monopole type antenna applicable to WLAN and WiMAX standard frequency bands is designed and fabricated. The proposed antenna is designed to have rectangular ring and rectangular patch based on microstrip feeding for triple band characteristics and inserted two stub in the top of the rectangular ring patch to enhance impedance bandwidth characteristics. The proposed antenna has $18.0mm(2W_1+W_2){\times}33.0mm(L_7+L_8+L_9)$ on a dielectric substrate of $27.0mm(W_1){\times}44mm(L_1){\times}1.0mm$ size. From the fabrication and measurement results, impedance bandwidths of 660MHz (2,08 to 2.74GHz) for 2.4/2.5MHz band, 488MHz (3.40 to 3.88GHz) for 3.5MHz band, and 2,180MHz (4.61 to 6.79GHz) for 5,000MHz band were obtained based on the impedance bandwidth. The proposed antenna also obtained the measured gain and radiation pattern in the anechoic chamber.