• Environmental Analysis Health and Toxicology
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• v.25 no.3
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• pp.197-205
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• 2010

The ecotoxicological effects of two antibiotics, fenbendazole and lincomycin, were observed in the harpacticoid copepod Tigriopus japonicus s.l. collected from tidal pools in the southern coast of Korea. Fenbendazole had a significant effect on the survival rates (p < 0.05), delay of copepodite emergence, and urosome size (p < 0.05). Lincomycin, on the other hand, had no significant influence on these environmental indicators. However, our analysis of morphological abnormalities in T. japonicus s.l. showed that lincomycin was more effective than fenbendazole in causing deformities. The pattern of deformity was diverse, with fused segments, and loss or addition of setae in the swimming legs. All of these patterns appeared as a result of relatively low concentrations of this antibiotic (0.3, $1\;{\mu}g\;L^{-1}$). We report here patterns of morphological abnormality in T. japonicus s.l. exposed to antibiotics, and suggest their possible application in ecotoxicological monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.198-198
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• 2017

Climate change causes unpredictable and erratic climatic patterns which affects crop production in agriculture and threatens public health. To cope with the challenges of climate change, sustainable and sound growth environment for crop production should be secured. Recent attention has been given to the development of smart irrigation system using sensors and wireless network as a solution to achieve water conservation as well as improvement in crop yield and quality with less water and labor. This study developed the smart irrigation technique for farmlands by monitoring the soil moisture contents and real-time climate condition for decision-making support. Central to this design is micro-controller which monitors the farm condition and controls the distribution of water on the farm. In addition, a series of laboratory studies were conducted to determine the optimal irrigation pattern, one time versus plug time. This smart technique allows farmers to reduce water use, improve the efficiency of irrigation systems, produce more yields and better quality of crops, reduce fertilizer and pesticide application, improve crop uniformity, and prevent soil erosion which eventually reduce the nonpoint source pollution discharge into aquatic-environment.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Journal of the Korean Geophysical Society
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• v.7 no.4
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• pp.269-276
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• 2004

Optical fiber sensors have shown a potential to serve real time health monitoring of the structures. They can be easily embedded or attached to the structures and are not affected by the electro-magnetic field. Furthermore, they have the flexibility of the sensor size and very highly sensitive. In this study, we conducted several laboratory and field tests using a novel optical sensor based on Brillouin scattering. One of the advantages of this technique is that the bare fiber itself acts as sensing element without any special fiber processing or preparation. Test results have shown that BOTDR can be a great solution for sensor systems of Civil Engineering Smart Structures.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.49-59
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• 2018

Wireless sensors are more favorable in measuring structural response compared to conventional sensors. This is because they are easier to use with no issues with cables and are considerably cheaper. There are several applications that can be used in recording and analyzing data from MEMS sensor installed on an iPhone. The Vibration App is one of the applications used and there has not been adequate research conducted in analyzing the performance of this App. This paper analyzed the performance of the Vibration App by comparing it with the performance of an ICP sensor. Results show that natural frequency results are more accurate (error less than 5%) in comparison to the amplitude results. This means that built- in MEMS sensor in smartphones are good at estimating natural frequency of structures. In addition, it was seen that the results became more accurate at higher frequencies (5.0Hz and 10.0Hz).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.409-412
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• 2019

국내의 미세먼지 문제가 심각해짐에 따라 대기 오염에 관한 분야의 관심이 높아지고 있다. 현재 정부는 최근 IT 융합 기술의 발전에 따라 빅데이터, 클라우드, 등 사물인터넷 기반 장치의 확산 및 고도화를 위한 기술 접목에 많은 지원과 관심을 보이며 기상청을 통해서는 국내 대기 오염으로 인한 사회적 비용을 낮추기 위해 공공 데이터(Application Program Interface, API)를 활용 다양한 정보 서비스를 지원하고 있다. 하지만 기상청에서 제공하는 정보 서비스에는 한계가 있다. 특히 기상청에서 운영되고 있는 장비들은 고가의 장비로써 비용 및 공간적 설치 제약이 따르며, 약 15km 범위를 한 개소로 담당하여 기상 데이터에 대한 신뢰도에 문제가 발생하고 있다. 본 논문에서는 오픈 하드웨어 기반 소형 기상관측 장비를 활용한 기상지수 및 미세먼지 측정 데이터 제공 시스템을 제안한다. 본 논문에서 제안한 시스템은 기상 계측이 필요한 지역의 작은 공간을 활용, 기상관측 장비를 통해 관측된 데이터와 기상청에서 제공하는 생활 기상지수 알고리즘을 토대로 해당 지역에 맞는 맞춤형 정보를 제공하여 사회적 비용을 낮출 수 있을 것으로 기대한다.

• Smart Structures and Systems
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• v.23 no.5
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• pp.421-428
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• 2019

The development of the low power application such as wireless sensors and health monitoring systems, attract a great attention to low power vibration energy harvesters. The recent vibration energy harvesters use smart materials in their structures to convert ambient mechanical energy into electricity. The frequent model of this harvesters is cantilevered beam. In the literature, the base excitation cantilevered harvesters are mainly investigated, and the related models are presented. This paper investigates a tip excitation cantilevered beam energy harvester with permendur. In the first section, the mechanical model of the harvester and magneto-mechanical model of the permendur are presented. Later, to find the maximum output of the harvester, based on the response surface method (RSM), some experiments are done, and the results are analyzed. Finally, to verify the results of RSM, a harvester with optimum design variables is made, and its output power is compared. The last comparison verifies the estimation of the RSM method which was about $381{\mu}W/cm^3$.

• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.202-206
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• 2023

Today, social media has become a vital tool. The world communicates and reaches the news and each other's opinions through social media accounts. Recently, considerable research has been done on analyzing social media due to its rich data content. At the same time, since the beginning of the COVID-19 pandemic, which has afflicted so many around the world, the search for a vaccine has been intense. There have been many studies analyzing people's feelings during a crisis. This study aims to understand people's opinions about available Coronavirus vaccines through a learning model that was developed for this purpose. The dataset was collected using Twitter's streaming Application Programming Interface (API) , then combined with another dataset that had already been collected. The final dataset was cleaned, then analyzed using Python. Polarity and subjectivity functions were used to obtain the results. The results showed that most people had positive opinions toward vaccines in general and toward the Pfizer one. Our study should help governments and decision-makers dispel people's fears and discover new symptoms linked to those listed by the World Health Organization.