• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.

• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.1
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• pp.8-16
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• 2024

Limestone (CaCO₃, calcium carbonate), which is used as a raw material in the portland cement and steel industry, emits CO₂ through decarbonation by high temperatures in the manufacturing process. To reduce CO₂ emissions by the use of raw materials like limestone, it has been proposed to replace limestone with various industrial by-products that contain CaO but less or none of the carbonated minerals, that cause CO₂ emissions. Loss of Ignition (LOI), Thermogravimetric analysis (TG), and Infrared Spectroscopy (IR) are used to quantitative the amount of CO₂ emission by using these industrial by-products, but CO₂ emissions can be either over or underestimated depending on the characteristics of by-product materials. In this study, we estimated CO₂ contents by LOI, TG, IR and DTG(Differential Thermogravimetric analysis) of calcite(CaCO₃) and samples that contain CO₂ in the form of carbonate and whose weight increases by oxidation at high temperatures. The test results showed for CaCO₃ samples, all test methods have a sufficient level of reliability. On the other hand, for the CO₂ content of the sample whose weight increases at high temperature, LOI and TG did not properly estimate the CO₂ content of the sample, and IR tended to overestimate compared to the predicted value, but the estimated result by DTG was close to the predicted valu e. From these resu lts, in the case of samples that contain less than a few percent of CO₂ and whose weight increases during the temperature that carbonate minerals decompose, estimating the CO₂ content using DTG is a more reasonable way than LOI, TG, and IR.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.6
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• pp.737-747
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• 1999

To estimate biomass and distribution of the Antarctic krill (Euphausia superba), hydroacoustic survey was conducted on board of R/V Yuzhmorgeologiya, which was chartered by Korea Antarctic Research Program (KARP) group from 18 to 21 December 1998, in the northern part of the South Shetland Islands, Antarctic Ocean, The scientific echo sounder (towing body type) used was EK- 500 (SIMRAD, Norway) with echo integrator (BI-500) at 38 kHz frequency and recorded mean backscattering cross-section coefficient (SA) per 1 $mile^2$ of sea surface. Also, Bongo net sampling was carried out to determine the size of krill and CTD (Conductivity, Temperature and Depth) casting to understand physical structure. Water column was divided into 5 layers (22$\~$65 m, 65$\~$115 m, l15$\~$65 m, 165$\~$215 m and 215$\~$315 m) to know vertical distribution of krill biomass. The standard length of krill collected was between 30 mm and 51 mm, and adult krill had single mode (41 mm). Maximum horizontal length of krill patch was about 35 nautical mile and vertical thickness was about 275 m. High density of krill was appeared in frontal area between Circumpolar Deep Water (>$1^{\circ}C$) and very low temperature water mass (< $-0.5^{\circ}C$) that originate from Weddell Sea. According to the results calculated using target strength equation, krill density was totally higher in continental slope and open water areas than in coastal area. In the study area, krill seems to distribute in depth; density was low at first layer ($\={\rho}=17.0\;g/m^2$) and higher at fourth layer ($\={\rho}=40.19\;g/m^2$). The estimated krill biomass at total survey area and water column was about 2.77 million metric ion ($\={\rho}=151.0\;g/m^2$) and coefficient of valiance ( CV, $\%$) was 19.92. The proportions and biomass of krill biomass at each layer were as follows; layer 1 ($11.3\%$, 0.31 million metric ton, CV=16.24), layer 2 ($13.3\%$, 0.37 million metric ton, CV=34.91), layer 3 ($23.7\%$, 0.66 million metric ton, CV=41.5), layer 4 ($26.6\%$, 0.74 million metric ton, CV=27.84) and layer 5 ($25\%$, 0.69 million metric ton, CV= 26.83).

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Korean Journal of Environment and Ecology
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• v.34 no.5
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• pp.358-371
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• 2020

As climate change is expected to lead to a severe reduction of biodiversity, studies to investigate the reasons for habitat loss, growth decline, and death of Korean fir (Abies koreana Wilson), endangered alpine/subalpine species in Korea, have been conducted for years but found no clear answer yet. This study reviewed previous studies on Korean fir published in the journals in the past 40 years, 1980 through 2020, into 10-year units, examined the study trend by period, region, and subject with a focus on ecological studies, and analyzed the study results. The ecological studies were categorized into evolutionary ecology, physiological ecology, population ecology, and landscape ecology. Based on the results, we suggested the required research fields in the future. We found a total of 73 papers published in the past 40 years and 48 (65.8%) of them published in the past 10 years. In terms of region, Mt. Halla accounted for the most as 41 papers were on it. In terms of ecological subjects, the physiological ecology accounted for the most with 38, and the evolutionary ecology accounted for the least with 10. The review of the study results showed that many studies identified water stress caused by the water resource imbalance due to temperature increase and spring precipitation reduction following climate change as the main reason for the decline and habitat loss of Korean fir. However, recent studies suggested other factors, such as soil environment, disturbing organisms, and climatic events. The cause of the decline and death of the Korean fir not yet being clearly identified is that most of the studies dealt with the basic content, were carried out intermittently, and were concentrated in some regions. Therefore, we need long-term studies with advanced technology in each study subject at a local scale to find the cause of Korean fir decline and present sustainable management and conservation. Moreover, it is necessary to extend our study subjects to ecosystem ecology and systems ecology to integrate the results from various study subjects for a comprehensive understanding of the reason for Korean fir declines. The results of comprehensive studies could provide clearer answers for Korean fir's declines and the alternatives of conservation management and practices.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.28-33
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• 2016

Batteries are used for main power engine in the fields such as mobiles, electric vehicles and unmanned submarines, for starter and lamp driver in general automotive, for emergency electric source in ship. These days, lead-acid and the lithium ion batteries are increasingly used in the fields of the secondary battery, and the lead-acid battery has a low price and safety comparatively, The lithium ion battery has a high energy density, excellent output characteristics and long life, whereas it has the risk of explosion by reacting with moisture in the air. But Recently, due to the development of waterproof, fireproof, dustproof technology, lithium batteries are widely used, particularly, because their usages are getting wider enough to be used as a power source for hybrid ship and electric propulsion ship, it is necessary to manage more strictly. Hybrid ship has power supply units connected to the packets to produce more than 500kWh large power source, and therefore, A number of the communication modules and wires need to implement the wire inspection and monitor system(WIIMS) that allows monitoring server to transmit detecting voltage, current and temperature data, which is required for the management of the batteries. This paper implements a low price type wireless inspection and monitoring system(WILIMS) of the lithium ion battery for hybrid vessels using BLE wireless communication modules and power line modem( PLM), which have the advantages of low price, no electric lines compared to serial communication inspection systems(SCIS). There are state of charge(SOC), state of health(SOH) in inspection parts of batteries, and proposed system will be able to prevent safety accidents because it allows us to predict life time and make a preventive maintenance by checking them at regular intervals.

• Korean Journal of Ecology and Environment
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• v.40 no.2
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• pp.201-213
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• 2007

In this study, the Batter Assessment Science Integrating point and Nonpoint Sources (BASINS 3.0)/window interface to Hydrological Simulation Program-FPRTRAN (WinHSPF) was applied for assessment of Soyang Dam watershed. WinHSPF calibration was performed using monitoring data from 2000 to 2004 to simulate stream flow. Water quality (water temperature, DO, BOD, nitrate, total organic nitrogen, total nitrogen, total organic phosphorus and total phosphorus) was calibrated. Calibration results for dry-days and wet-days simulation were reasonably matched with observed data in stream flow, temperature, DO, BOD and nutrient simulation. Some deviation in the model results were caused by the lack of measured watershed data, hydraulic structure data and meteorological data. It was found that most of pollutant loading was contributed by nonpoint source pollution showing about $98.6%{\sim}99.0%$. The WinHSPF BMPRAC was applied to evaluate the water quality improvement. These scenarios included constructed wetland for controlling nonpoint source poilution and wet detention pond. The results illustrated that reasonably reduced pollutant loadin. Overall, BASINS/WinHSPF was found to be applicable and can be a powerful tool in pollutant loading and BMP efficiency estimation from the watershed.

• Proceedings of the Korea Society of Poultry Science Conference
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• 2004.11a
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• pp.80-90
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• 2004

Three experiments were performed to test the assumption that imbalanced dietary amino acid mixtures must lead to increased heat production (HP). The first experiment was based on diets formulated to have a wide range of crude protein (CP) concentrations but a fixed concentration of lysine, formulated to be the first-limiting amino acid. In the second (converse) experiment, lysine concentration was varied over a wide range while CP content was kept constant. To prevent the masking of dietary effects by thermoregulatory demands, the third experiment was performed at 30 $^{\circ}C$ with the diets similar to the diets used in the second experiment. The detailed relationships among amino acid balance, nitrogen (N) metabolism and energy (E) metabolism were investigated in a computer-controlled chamber calorimetry system. The results of experiments were compared with the predictions of a computerised simulation model of E metabolism. In experiment 1. with constant lysine and varying CP, there was a 75 % increase in N intake as CP concentration increased. This led to a 150 % increase in N excretion. with no significant change in HP. Simulated HP agreed with the empirically determined results in not showing a trend with dietary CP. In experiment 2, with varying lysine but constant CP, there was a 3-fold difference in daily weight gain between the lowest and highest lysine diets. HP per bird increased significantly with dietary lysine concentration. There was still an effect when HP was adjusted for body weight differences, but it failed to maintain statistical significance. Simulated HP results agreed in showing little effect of varying lysine concentration and growth rate on HP. Based on the results of these two experiments, the third experiment was designed to test the response of birds to dietary lysine in high ambient temperature. In experiment 3 which performed at high ambient temperature (30 $^{\circ}C$), HP per bird increased significantly with dietary lysine content, whether or not adjusted for body-weight. The trend was greater than in the previous experiment (20 $^{\circ}C$).