• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.283-291
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• 2011

As the construction industry has recently been transformed by the emergence of ubiquitous and intelligent technology, there have been major changes in the management methods employed. Specifically, next-generation construction management systems have been developed that collect and analyze many pieces of information in real time by using various wireless sensors and networks. The purpose of this study is to understand the current status of Ubiquitous Sensor Networks (USN) in the construction sector, and to gain fundamental data for a system of measuring concrete curing temperature in a construction site that employs a USN. By investigating the application status of USN, it was confirmed that USN has mainly been applied to the maintenance of facilities, safety management, and quality control. In addition, a field experiment in which the curing temperature of concrete was measured using a USN was carried out to evaluate two systems with wireless sensor networks, and the applicability of these systems on site was confirmed. However, it is estimated that the embedded wireless sensor type is affected by metal equipment on site, internal battery of sensor and concrete depth, and studies to provide more stable system by USN are thus required.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.520-531
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• 2022

Purpose: Based on the risk evaluation of hydrogen fueling stations, this study aims to find a plan to strengthen management safety by examining profitability and management risk, which are major concerns of employers. Method: The risk evaluation was divided into 'acceptable risk' and 'allowable risk' over time from the stage of installation of hydrogen fueling stations, and compared and analyzed with the results of existing studies. Result: Existing studies have been appropriately applied to the risk assessment performed at the stage of installing hydrogen fueling stations. However, possible risks could be found at the operational stage. In other words, it was derived that an evaluation of management risk was also necessary. And through this, it was confirmed that the safety of hydrogen fueling stations was strengthened. Conclusion: The risk assessment that precedes the stage of installing hydrogen fueling stations is appropriate because significant results have been derived from the 'acceptable risk' assessment. However, the operator needs to evaluate the risks that may occur at the operating stage, that is, the 'allowable risks' and prepare countermeasures. Therefore, it is proposed to add management risk assessment items to build and operate safer hydrogen fueling stations.

• Journal of IKEEE
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• v.18 no.4
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• pp.552-558
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• 2014

This paper suggests how to design a ZigBee-chip-based communication module to remotely measure radiation level. The suggested communication module consists of two control processors for the chip as generally required to configure a ZigBee system, and one chip module to configure a ZigBee RF device. The ZigBee-chip-based communication module for remote radiation measurement consists of a wireless communication controller; sensor and high-voltage generator; charger and power supply circuit; wired communication part; and RF circuit and antenna. The wireless communication controller is to control wireless communication for ZigBee and to measure radiation level remotely. The sensor and high-voltage generator generates 500 V in two consecutive series to amplify and filter pulses of radiation detected by G-M Tube. The charger and power supply circuit part is to charge lithium-ion battery and supply power to one-chip processors. The wired communication part serves as a RS-485/422 interface to enable USB interface and wired remote communication for interfacing with PC and debugging. RF circuit and antenna applies an RLC passive component for chip antenna to configure BALUN and antenna impedance matching circuit, allowing wireless communication. After configuring the ZigBee-chip-based communication module, tests were conducted to measure radiation level remotely: data were successfully transmitted in 10-meter and 100-meter distances, measuring radiation level in a remote condition. The communication module allows an environment where radiation level can be remotely measured in an economically beneficial way as it not only consumes less electricity but also costs less. By securing linearity of a radiation measuring device and by minimizing the device itself, it is possible to set up an environment where radiation can be measured in a reliable manner, and radiation level is monitored real-time.

• Journal of Bio-Environment Control
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• v.19 no.4
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• pp.266-274
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• 2010

This study aim to investigate fundamentally the growth and physiological responses of tomato plants in responses to two different levels of water deficit, a weak drought stress (-25 kPa) and a severe drought stress (-100 kPa) in soil. The two levels of water deficit were maintained using a micro-irrigation system consisted of soil sensors for the real-time monitoring of soil water content and irrigation modules in a greenhouse experiment. Soil water contents were fluctuated throughout the 30 days treatment period but differed between the two treatments with the average -47 kPa in -25 kPa set treatment and the -119 kPa in -100 kPa set treatment. There were significant differences in plant height between the two different soil water statuses in plant height without differences of the number of nodes. The plants grown in the severe water-deficit treatment had greater accumulation of biomass than the plants in the weak water-deficit treatment. The severe water-deficit treatment (-119 kPa) also induced greater leaf area and leaf dry weight of the plants than the weak water-deficit treatment did, even though there was no difference in leaf area per unit dry weight. These results of growth parameters tested in this study indicate that the severe drought could cause an adaptation of tomato plants to the drought stress with the enhancement of biomass and leaf expansion without changes of leaf thickness. Greater relative water content of leaves and lower osmotic potential of sap expressed from turgid leaves were recorded in the severe water deficit treatment than in the weak water deficit treatment. This finding also postulated physiological adaptation to be better water status under drought stress. The drought imposition affected significantly on photosynthesis, water use efficiency and stomatal conductance of tomato plants. The severe water-deficit treatment increased PSII activities and water use efficiency, but decreased stomatal conductance than the weak water-deficit treatment. However, there were no differences between the two treatments in total photosynthetic capacity. Finally, there were no differences in the number and biomass of fruits. These results suggested that tomato plants have an ability to make adaptation to water deficit conditions through changes in leaf morphology, osmotic potentials, and water use efficiency as well as PSII activity. These adaptation responses should be considered in the screening of drought tolerance of tomato plants.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.733-739
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• 2016

Insolation is an major indicator variable that can serve as an energy source in earth system. It is important to monitor insolation content using remote sensing to evaluate the potential of solar energy. In this study, we performed sensitivity analysis of observed frequency on daily composite insolation over the Korean peninsula. We estimated INS through the channel data of Communication, Ocean and Meteorological Satellite (COMS) and Cloud Mask which have temporal resolution of 1 and 3 hours. We performed Hemispherical Integration by spatial resolution for meaning whole sky. And we performed daily composite insolation. And then we compared the accuracy of estimated COMS insolation data with pyranometer data from 37 points. As a result, there was no great sensitivity in the daily composite INS by observed frequency of satellite that accuracy of the calculated insolation at 1 hour interval was $28.6401W/m^2$ and 3 hours interval was $30.4960W/m^2$. However, there was a great difference in the space distribution of two other INS data by observed frequency of clouds. So, we performed sensitivity analysis with observed frequency of clouds and distinction between the two other INS data. Consequently, there was showed sensitivity up to $19.4392W/m^2$.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.154-155
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• 2011

2010년 천리안위성의 성공적인 발사에 따라 인공위성의 활용에 대한 기대가 커지고 있다. 천리안 해양관측위성(GOCI)이외에 아리랑 2호가 현재 운용중인 우리나라 위성들이다. 가까운 시기에 아리랑 5호(2011년 말), 아리랑 3호(2012년), 아리랑 3A호(2013년)가 발사될 예정이다. 즉, 해양적용을 위한 위성환경은 이제부터 준비되고 있다고 볼 수 있다. 대외적으로 보면, 인공위성 자원은 아주 많다. 문제는 이와 같은 자원을 어떻게 활용할 것인가 인데 이의 활용 기술 개발적 측면에서는 많이 소홀한 것이 사실이다. 전세계적으로 이 시스템 개발을 위한 치열한 경쟁이 진행 중에 있다. 이미 소말리아 주변 감시체계는 많은 부분을 위성에 의지하고 있다. 우리나라에서 최초로 위성활용 가능성을 보여준 사건이 허베이스피리트호 원유유출 사고이다. 이 사고는 2007년 12월7일 아침 7시6분경 서해안 만리포 북서쪽 10km 해상에서 크레인을 적재한 1만1800t급 바지선이 정박 중인 홍콩 선적 유조선 허베이 스피리트호(14만6000t급)와 부딪치면서 발생했다. 이와 같은 기름 유출 사고의 경우, 유출 범위를 정확하게 이해하는 것이 중요하다. 거의 준비된 상태가 아님에도 불구하고 12월 8일 아침 최초로 유출된 기름을 모습을 보여주는 위성이미지(광학위성)가 얻어졌다. 하지만 이와 같은 자료가 관련 전문가가 이용할 수 있기까지 많은 시간이 소용되었고, 이 정보를 전달할 수 있는 방법도 없었다. 사실 단순한 이미지가 아니라 지리정보체계를 가진 오염정보를 제공할 방법도 준비도 되어 있지 못한 상황이었다. 본 발표를 통하여, 허베이스피리트호 사고뿐만 아니라, 2011년 6월부터 수개월간 지속된 발해만 오염사고 적용 등 다양한 사례 소개를 하고, 이를 기반으로 해양경찰청에서 업무활용을 위한 방안을 제시한다. 먼저, 해경청의 주요 임무인, 경비, 수색구조, 오염대응 분야별로 현황 분석을 수행하였다. 또한 국외사례에 대한 조사를 한 후, 최종 인공위성 원격탐사기술의 해경청 도입방안에 대한 설계를 실시하였다. 국제적으로 인공위성을 이용한 해양 경비, 수색구조, 오염 모니터링기술 개발이 이루어지고 있으며, 유럽 국가는 시범도입을 진행 중에 있다. 유럽해사안전국(EMSA)은 해양경비 및 수색구조를 위한 선박통항 및 보고 서비스와 오염대비대응(Pollution Preparedness and Response, PPR) 위성 서비스를 회원국에 제공하고 있다. 해양경찰청 임무 수행뿐만 아니라, 해양영토 관리적 측면에서 첨단 위성장비 활용, 선진국형 해상경비 패러다임의 전환 필요성이 크다고 할 수 있다.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.431-440
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• 2005

Local K-index is an indicator representing local geomagnetic activity in every 3 hour. For estimation of the local K-index, a reasonable determination of solar quiet curve (undisturbed daily variation of geomagnetic field) is quiet essential. To derive the solar quiet curve, the FMI method, which is one of representative algorithms, uses horizontal components (H and D) of 3 days magnetometer data from the previous day to the next day for a specific day. However, this method is not applicable to real time forecast since it always requires the next day data. In this study, we have devised a new method to estimate local K-index in near real-time by modifying the FMI method. The new method selects a recent quiet day whose $K_p$ indices, reported by NOAA/SEC are all lower than 3, and replace the previous day and the next day data by the recent quiet day data. We estimated 2,672 local K indices from Gyeongzu magnetometer in 2003, and then compared the indices with those from the conventional FMI method. We also compared the K indices with those from Kakioka observatory. As a result, we found that (1) K indices from the new method are nearly consistent with those of the conventional FMI method with a very high correlation (R=0.96); (2) onr local K indices also have a relatively high correlation (R=0.81) with those from Kakioka station. Our results show that the new method can be used for near real-time estimation of local K indices from Gyeongzu magnetometer.