• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1161-1170
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• 2019

In this study, we propose a model for predicting Cochlodinium polykrikoides red tide occurrence using deep neural networks. A deep neural network with eight hidden layers was constructed to predict red tide occurrence. The 59 marine and meteorological factors were extracted and used for neural network model training using satellite reanalysis data and meteorological model data. The red tide occurred in the entire dataset is very small compared to the case of no red tide, resulting in an unbalanced data problem. In this study, we applied over sampling with adding noise based data augmentation to solve this problem. As a result of evaluating the accuracy of the model using test data, the accuracy was about 97%.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.1
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• pp.65-71
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• 2003

Infrared heating has been traditionally used in industrial applications for processes such as dehydration of food industrial. This heating method involves the application of radiation in the wavelength range of 5~50 micrometers. In this work, simultaneous heat balance equations were developed to simulate the infrared radiation heating of red peppers. The equations assume that moisture diffuses to the outer boundaries of the material in liquid form and evaporation occurs at the surface of the red peppers. Energy for moisture evaporation is supplied by the infrared radiant energy. The equations were validated with experimental data on surface temperature and average moisture content of red peppers. Average deviations of predicted surface red peppers temperature and average red peppers moisture from experimental data were 323~353K and 50~80%, respectively. The spectral extinction coefficients in the wavelength range $1.5<{\lambda}<27$ micrometer at 293K for Red Peppers were determined from results of reflection measurements and the four flux radiative heat transfer calculation. The radiation extinction coefficients were obtained from effective drying factor the temperature 373K.

• Journal of Communications and Networks
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• v.7 no.3
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• pp.337-352
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• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.654-655
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• 2003

Korea has experienced 10 a Cochlodinium polykrikoides red tide outbreaks during the last 10 years (1993-2002). The monitoring activities at National Fisheries Research and Development Institute (NFRDI) in Korea have been extended to all the coastal waters after the worst of fish killing by C. polykrikoides blooms in 1995. NFRDI is looking forward to finding out the feasibility of red tide detection around Korean waters using satellite remote sensing of NOAA/AVHRR, Orbview-2/SeaWiFS, IRS-P4/OCM and Terra/MODIS on real time base. In this study, we used several alternative methods including climatological analysis, spectral and optical methods which may offer a potential detection of the major species of red tide in Korean waters. The relationship between the distribution of SST and C. polykrikoides bloom areas was studied. In climatological analysis, NOAA, SeaWiFS, OCM satellite data in 20th and 26th August 2001 were chosen using the known C. polykrikoides red tide bloom area mapped by helicopter reconnaissance and ground observation. The 26th August, 2001 SeaWiFS chlorophyll a anomaly imageries against the imageries of non-occurring red tide for August 20, 2001 showed the areas C. polykrikoides occurred. The anomalies of chlorophyll a concentration from satellite data between before and after red tide outbreaks showed the similar distribution of C. polykrikoides red tide in 26th August, 2001. The distribution of the difference in SST between daytime and nighttime also showed the possibility of red tide detection. We used corrected vegetation index (CVI) to detect floating vegetation and submerged vegetation containing algal blooms. The simple result of optical absorption from C. polykrikoides showed that if we use the optical characteristics of each red tide we will be able to get the feasibility of the red tide detection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.256-264
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• 2023

Smart farm technology using IoT is one of the technologies that can increase productivity and improve the quality of agricultural products in agriculture, which is facing difficulties due to the decline in rural population, lack of rural manpower due to aging, and increase in diseases and pests due to climate change. . Smart farms using existing IoT simply monitor farms, implement smart plant growers, and have automatic greenhouse opening and closing systems. This paper implements a smart farm system based on MQTT, an industry standard protocol for the Internet of Things, and Node-RED, a representative development middleware for the Internet of Things. First, data is extracted from Arduino sensors, and data is collected and transmitted from IoT devices using the MQTT protocol. Then, Node-RED is used to process MQTT messages and store the sensing data in real time in MongoDB, a representative NoSQL, to store the data. Through this smart farm system, farm managers can use a computer or mobile phone to check sensing information on the smart farm in real time, anytime, anywhere, without restrictions on time and space.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.817-825
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• 2003

The amount of damage suffered from the red tide occurring at the near shore is increasing rapidly. The Windows visual system discussed in this paper is developed in order to help minimize the damage. The system is focused on the monitoring the coastal environment. and forecasting the red tide occurrence. Although several similar systems are now existing. most of them are based on the web application. which cause the large response time. limited presentation ability of data. and inability of data storing at client side. The Windows visual system described in this paper operates on the Internet to get the ubiquitous access. One of three components of the Windows visual system. client system is developed as a Windows application in order to overcome the weak points of the previous systems. The gathering. analysis, and monitoring of data can be done at real time using the Windows visual system.

• Fashion & Textile Research Journal
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• v.3 no.3
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• pp.204-210
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• 2001

The purpose of the study is to clarify red image in the modem fashion. 40 kinds of costume samples being visual power in red have been selected from photographs in fashion magazines and divided into tones: pale (Vp, Lgr, L), bright (P, B), vivid (S, B, Dp), dark (Gr, Dl, Dgr, Dk). The study was measured by using Semantic Differential method. The subjects were 50 students majoring in clothing and textile. The data were analyzed by factor analysis, ANOVA, discrimminant analysis, MDS and regression analysis. The results of analysis are as follow; 1. Factor analysis has extracted 5 factors of red image in the fashion. These factor are Attractiveness, Hardness and Softness, Emotion, Attention, Simplicity. 2. There were significant difference in visual evaluation of red tones. 3. The discrimination among 4 red tones was related to attention and weight of red. 4. Evaluative dimensions of red was classified as Soft-Hard, Lively-Decent. 5. The image effect on Preference, Buying needs, Pleasant and Riches was consist of complicated sensibility.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.279-284
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• 2004

Red tide(harmful algae) in the Korean Coastal Waters has a given a great damage to the fishery every year. However, the aim of our study understands the influence of meteorological factors (air and water tempaerature, precipitation, sunshine, solar radiation, winds) relating to the mechanism of red tide occurrence and monitors red tide by satellite remote sensing, and analyzes the potential area for red tide occurrence by GIS. The meteorological factors have directly influenced on red tide formation. Thus, We want to predict and apply to red tide formation from statistical analyses on the relationships between red tide formation and meteorological factors. In future, it should be realized the near real time monitoring for red tide by the development of remote sensing technique and the construction of integrated model by the red tide information management system (the data base of red tide - meteorological informations).

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.199-205
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• 2021

The distributing board in directly installed on the ground or the bottom surface of the building, and when vibrations such as earthquakes or external shocks occur, the possibility of damage or malfunction of electric components such as internal power devices, wiring, and protection relays increases. Recently, the need for a seismic type distributing board is increasing, and research and development of a distributing board having a vibration damping function and product launch are being conducted. In this paper, an IoT-based data collection device system capable of measuring vibration and environmental data of distributing board was designed and implemented. When vibration occurred on the distributing board, data was stored and visualized in the MySQL DB through Node-RED for monitoring and data storage using the MQTT protocol for reliable messaging transmission. The test was conducted by attaching the IoT device of the distributing board, and data was collected in real-time and monitored through Node-RED.