• Journal of fish pathology
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• v.17 no.1
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• pp.57-66
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• 2004

Effects of oral administration with fermented product from sewage in land-based seawater fish farm on haematological disturbance in the olive flounder, Paralichthys olivaceus was investigated. After 4 weeks of conditioning with a basal diet, fish were divided into 4 groups and provided experimental diet (0.1, 0.5, 1.0 and 2.0%) supplement of fermented sewage for 80 days. Proximal analysis was performed for the product of sewage which was fermented by lactic acid and yeast. RBC count, hemoglobin concentration and hematocrit value were increased according to the treated periods, however, no statistical difference was observed between control and treatment groups. There were no significant difference in serum organic, inorganic compounds and enzyme activities between control and treatment groups. This study hypothesized that the supplement of fermented product from sewage in land-based seawater fish farm might be an additive supplement for source of fish diet in view of haematological examination. Recycling of the sewage may be an economic artificial sources of diet for fish aquaculture practices.

• 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 Safety
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• v.36 no.5
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• pp.1-9
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• 2021

With the convergence of the information and communication technologies, a new age of technological civilization has arrived. This is the age of intelligent revolution, known as the 4th industrial revolution. The 4th industrial revolution is based on technological innovations, such as robots, big data analysis, artificial intelligence, and unmanned transportation facilities. This revolution would interconnect all the people, things, and economy, and hence will lead to the expansion of the industry. A high-density, high-capacity energy technology is required to maintain this interconnection. As a next-generation energy source, lithium-ion batteries are in the spotlight today. However, lithium-ion batteries can cause thermal runaway and fire because of electrical, thermal, and mechanical abuse. In this study, thermal runaway was induced in 72.5 Ah NCM pouch-type lithium-ion batteries because of thermal abuse. The surface of the pouch-type lithium-ion batteries was heated by the hot plate heating method, and the effect of the rate of increase in the surface temperature on the thermal runaway trigger time was analyzed using Minitab 19, a statistical analysis program. The correlation analysis results confirmed that there existed a strong negative relationship between each variable, while the regression analysis demonstrated that the thermal runaway trigger time of lithium-ion batteries can be predicted from the rate of increase in their surface temperature.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Biomolecules & Therapeutics
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• v.27 no.3
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• pp.265-275
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• 2019

Technological advances of mankind, through the development of electrical and communication technologies, have resulted in the exposure to artificial electromagnetic fields (EMF). Technological growth is expected to continue; as such, the amount of EMF exposure will continue to increase steadily. In particular, the use-time of smart phones, that have become a necessity for modern people, is steadily increasing. Social concerns and interest in the impact on the cranial nervous system are increased when considering the area where the mobile phone is used. However, before discussing possible effects of radiofrequency-electromagnetic field (RF-EMF) on the human body, several factors must be investigated about the influence of EMFs at the level of research using in vitro or animal models. Scientific studies on the mechanism of biological effects are also required. It has been found that RF-EMF can induce changes in central nervous system nerve cells, including neuronal cell apoptosis, changes in the function of the nerve myelin and ion channels; furthermore, RF-EMF act as a stress source in living creatures. The possible biological effects of RF-EMF exposure have not yet been proven, and there are insufficient data on biological hazards to provide a clear answer to possible health risks. Therefore, it is necessary to study the biological response to RF-EMF in consideration of the comprehensive exposure with regard to the use of various devices by individuals. In this review, we summarize the possible biological effects of RF-EMF exposure.

• Geophysics and Geophysical Exploration
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• v.21 no.4
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• pp.231-243
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• 2018

Streaming potential (SP) arises from fluid flow through effectively connected pores. From this potential, formation water information as well as fluid flow properties can be estimated. As micro particles being located in boundary between subsurface porous media and fluid are charged to form electrical double layer, fluid flow caused by several reasons generates SP, one of electrokinetic phenomena. Occurrence mechanism of SP is complex and signal strength is relatively weak compared to noise. However, application of self potential survey using SP to monitoring of formation fluid is expanding because of its' convenience of exploration without artificial source and repetitiveness of signal. This paper accounts for the occurrence mechanism of SP studied before, including governing equations and analyzes previous various case studies of SP according to the change of physical properties of materials. It helps to increase understanding about SP and also lays the foundations of the application of SP to fields.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.39-55
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• 2019

In this study, we have proposed an improved method to detect forest fires by correcting the reflected signals of day images using the middle-wavelength infrared (MWIR) channel. The proposed method is allowed to remove the reflected signals only using the image itself without an existing data source such as a land-cover map or atmospheric data. It includes the processing steps for calculating a solar-reflected signal such as 1) a simple correction model of the atmospheric transmittance for the MWIR channel and 2) calculating the image-based reflectance. We tested the performance of the method using the MODIS product. When compared to the conventional MODIS fire detection algorithm (MOD14 collection 6), the total number of detected fires was improved by approximately 17%. Most of all, the detection of fires improved by approximately 30% in the high reflection areas of the images. Moreover, the false alarm caused by artificial objects was clearly reduced and a confidence level analysis of the undetected fires showed that the proposed method had much better performance. The proposed method would be applicable to most satellite sensors with MWIR and thermal infrared channels. Especially for geostationary satellites such as GOES-R, HIMAWARI-8/9 and GeoKompsat-2A, the short acquisition time would greatly improve the performance of the proposed fire detection algorithm because reflected signals in the geostationary satellite images frequently vary according to solar zenith angle.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.687-699
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• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.