• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.371-378
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• 2014

As people are taking part in more versatile social activities, it becomes more frequent and more important for people to share personal information each other in appropriate level. Unfortunately, although the rapid spread of smart devices and advance of network technologies have brought many applications for information sharing into our hands, they do not provide effective mechanism for sharing personal information on collocated people. In this paper, we introduce an android application named WhoAmI which provides the functionality of sharing personal information on nearby users over Wi-Fi Direct as well as Wi-Fi network environment. According to the predefined access level such as business, community or friend, WhoAmI naturally provides profile information to accessible users. In addition, the information such as music, photo, movie can be effectively shared through the application.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.529-539
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• 2017

The purpose of this study is to categorize and analyze the communication competency of engineering undergraduates and to develop educational content in order to improve that. In this study, communication competency of engineering undergraduates was categorized into three areas: critical thinking, scientific communication, and media literacy. As a means to improve communication competency, the experience with and perception of writing were investigated. The communication competency of undergraduates needs to be improved overall. There is a high need for writing programs that enhance critical thinking oriented around practice. It suggests flipped learning based on smart education, E-community, problem-solving programs based on action learning, cooperative learning programs, reflection journals & portfolio, and collaborative writing programs as educational content. The results of this study can be used as basic data to design competency-based communication curriculum and practical applications for engineering undergraduates.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.27-37
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• 2021

Conversion of rice paddy field to upland has been accelerated as the central government incentivizes more profitable upland crop cultivation. The objective of this study was to investigate the current status and conversion trend from paddy to upland for the reservoir irrigation districts. Total 605 of reservoir irrigation districts whose beneficiary area is greater than 200 ha were selected for paddy-to-upland conversion analysis using the land cover maps provided by the EGIS of the Ministry of Environment. The land cover data of 2019 was used to analyze up-to-date upland conversion status and its correlation with city proximity, while land cover change between 2007 and 2019 was used for paddy-to-upland conversion trend analysis. Overall 14.8% of the entire study reservoir irrigation area was converted to upland cultivation including greenhouse and orchard areas. Approximately the portion of paddy area was reduced by 17.8% on average, while upland area was increased by 4.9% over the 12 years from 2007 to 2019. This conversion from paddy to upland cultivation was more pronounced in the Gyoenggi and Gyeongsang regions compared to other the Jeolla and Chungcheong provinces. The increase of upland area was also more notable in proximity of the major city. This study findings may assist to identify some hot reservoir districts of the rapid conversion to upland cultivation and thus plan to transition toward upland irrigation system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.23-32
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• 2021

For agricultural water management, it is essential to establish the digital infrastructure data such as agricultural watershed, irrigated area and canal network in rural areas. Approximately 70,000 irrigation facilities in agricultural watershed, including reservoirs, pumping and draining stations, weirs, and tube wells have been installed in South Korea to enable the efficient management of agricultural water. The total length of irrigation and drainage canal network, important components of agricultural water supply, is 184,000 km. Major problem faced by irrigation facilities management is that these facilities are spread over an irrigated area at a low density and are difficult to access. In addition, the management of irrigation facilities suffers from missing or errors of spatial information and acquisition of limited range of data through direct survey. Therefore, it is necessary to establish and redefine accurate identification of irrigated areas and canal network using up-to-date high resolution images. In this study, previous existing data such as RIMS (Rural Infrastructure Management System), smart farm map, and land cover map were used to redefine irrigated area and canal network based on appropriate image data using satellite imagery, aerial imagery, and drone imagery. The results of the building the digital infrastructure in rural areas are expected to be utilized for efficient water allocation and planning, such as identifying areas of water shortage and monitoring spatiotemporal distribution of water supply by irrigated areas and irrigation canal network.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.11-21
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• 2022

The COVID-19 started to spread at early 2020 in South Korea and has been threatening our life in many aspects. Countermeasures such as social distancing to prevent COVID-19 spread have brought many changes in our society an human life. In this study, as a part of the COVID-19 pandemic management, drinking water usage change is analyzed to evaluate potential risks on water supply service. We collected hourly water use data of residential area in S city, which is a mid-size city in South Korea, before and after the COVID-19 pandemic. The collected data were analyzed to reveal the changes in total water consumption, water usage weight, and hourly water-demand pattern caused by the COVID-19 pandemic. The case study revealed the noticeable changes in water consumption caused by the COVID-19 pandemic and required more secured and adaptive operation of drinking water system under the pandemic situation caused by infectious disease.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.77-88
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• 2022

This study aims to understand the relationship between the distribution of fish species in the two water ecosystems and the habitat factors according to the survey period targeting Gwanggyo Lake Park in the city. There are studies on the appearance and distribution of species by applying eDNA to freshwater ecosystems. However, in the domestic, streams are the target, and studies on the relationship between species distribution and habitat environment in two water environments are lacking. We conducted to analyze the species list and relationship with habitat factors using eDNA research in May and October at 21 points in Gwanggyo Lake Park, Suwon City, which were connected to lakes and streams. As a result, there was no species difference in the water environment according to the survey period. However, the total number of reads during the spawning season(May) was 3,126,482, which was more than double that after the spawning season(October). Tolerant species appeared in Woncheon Lake with a slow or stagnant flow, but there was no significant correlation between species and habitat factors depending on the survey period. On the other hand, intermediate and sensitive species appeared in the Woncheon stream with high flow. There was a significant correlation between the low temperature during the spawning season and the high dissolved oxygen content after the spawning season(P<0.001, Tem.: 20.7±2.6℃, DO: 8.6±1.7). It is expected that environmental DNA will be used to survey species and suggest monitoring methods according to the survey period.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.