• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.5
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• pp.396-404
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• 2015

The UAV's wing has high aspect ratio that is suitable for the high altitude and long endurance. Knowing the real-time deformation of wing structure in flight, it can be utilized in structural health and loading status monitoring, improvement of control effectiveness and extraordinary vibration phenomena using displacement-strain relationship. In this paper, nonlinear displacement prediction algorithm was developed for prediction of large structural deflection in flight. The algorithm was validated through the comparison with finite element analysis results and also experimental results for several large tip displacements of cantilever beam. The predicted displacements using strains are agreed well with the measured values from laser displacement sensor.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.27-37
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• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1423-1431
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• 2018

IoT(Internet of Things) is applied to technologies such as agriculture and dairy farming, making it possible to cultivate crops easily and easily in cities.In particular, IoT technology that intelligently judge and control the growth environment of cultivated crops in the agricultural field is being developed. In this paper, we propose a method of predicting the growth environment of plants by learning the moisture supply cycle of plants using the intelligent object internet. The proposed system finds the moisture level of the soil moisture by mapping learning and finds the rules that require moisture supply based on the measured moisture level. Based on these rules, we predicted the moisture supply cycle and output it using media, so that it is convenient for users to use. In addition, in order to reduce the error of the value measured by the sensor, the information of each plant is exchanged with each other, so that the accuracy of the prediction is improved while compensating the value when there is an error. In order to evaluate the performance of the growth environment prediction system, the experiment was conducted in summer and winter and it was verified that the accuracy was high.

• Journal of the Korean Geotechnical Society
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• v.27 no.1
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• pp.25-34
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• 2011

In general, many instrumentations of slope rely on theory or experience because on-site accessibility and long term instrumentation are difficult to conduct the instrumentation of slopes. Also the prediction of disaster is very difficult. Therefore experimental research was conducted about an effective method to predict collapse of slope and on-site applicability in this study. The collapse of slope was able to be predicted by applying AE sensor which we call WEAD to the failure criteria. The parameters of AE generated during the collapse of slope were secured through bending shear test. Test construction was applied to the slope with a history and a possibility of collapse. As a result, it is shown that AE parameters do not exceed the failure criterion and is found to be stable slopes. As the real symptoms of collapse did not appear, AE was found to have excellent applicability.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.248-250
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• 2008

This paper proposes a prediction method to prevent traffic accident and reduce to vehicle waiting time using neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dose not consider coordinating green time. Moreover, we present neural network approach for traffic accident prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data. Computer simulation results proved reducing traffic accident waiting time which proposed neural network better than conventional system dosen't consider neural network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1845-1851
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• 2007

Background noise and room reverberation are two causes of degradation in speech in listening situations. Many algorithms developed to enhance reverberant speech. In this paper we propose a dereverberation method for enhancement of speech using modified the linear prediction(LP) residual in reverberant room condition. The proposed dereberberation method based on the fact that the signification excitation of the vocal tract system takes place at the instant of glottal closure in voiced speech. Our method used delay information form each sensor, and we need reverberant signals from 3 sensors. We obtain a new LP residual signal using modified IP residual combination which derived form weighting of the LP residual and the Hilbert transform of LP residual. The nature of the coherently added Hilbert envelop has several large amplitude spikes because of the effects of noise and reverberation. This residual of the clean speech is used to excite the time-varying all-pole filter to obtain the enhanced speech. We achieved simulation of proposed algorithm for performance analysis in reverberation environment. The proposed algorithm improves substantially the quality of reverberant speech.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2069-2076
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• 2008

In this paper, we develop an electrostatic monitoring system which is composed of an electrostatic prediction system and a warning message transmission system. The electrostatic prediction system in a factory receives the value of electrostatic charge from the electrostatic sensor and predicts the next value by using past data and sends the value to the warning message transmission system through the bluetooth communication. The warning message transmission system gets a warning signal and transmits the warning message to the worker's cellphone through a commercial SMS web by a socket program running on Windows PC in a control room. We propose electrostatic forecasting algorithms based on LSR(least square regression) using weight factors in an electrostatic prediction system. Simulation results show that the algorithm with dynamically variable weight factors is best with 64.69V standard deviation and a warning message transmitted by the warning message transmission system is displayed on cellphone after about 5 seconds.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.219-224
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• 2019

The number of solar power installation companies including domestic small scale (50kW or less) is increasing rapidly, but the efficient operation system and management are insufficient. Therefore, a new type of operating system is needed as a maintenance management aspect to maximize the generation amount in the current state rather than the additional function which causes the increase of the generation cost. In this paper, we utilize Big Data and sensor network to maximize the operating efficiency of solar power plant and analyze the expert system to develop power generation prediction technology, module unit fault detection technology, life prediction of inverter components and report technology, maintenance optimization And to develop a smart monitoring system that enables optimal operation of photovoltaic power plants such as development of algorithms and economic analysis.

• Smart Media Journal
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• v.11 no.5
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• pp.56-62
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• 2022

Water reservoir is defined as a storage space to hold and supply filtered water and it's significantly important to manage water level in the water reservoir so as to stabilize water supply by controlling water supply depending on demand. Liquid level sensors have been installed in the water reservoir and the pumps in the booster station facilitated management for optimum water level in the water reservoir. But the incident responses including sensor malfunction and communication breakdown actually count on manager's inspection, which involves risk of accidents. To stabilize draining facility management, this study has come up with AI model that predicts changes in the water level in the water reservoir. Going through simulation in the case of missing data in the water level to verify stability in relation to the field application of the prediction model for water level changes in the reservoir, the comparison of actual change value and predicted value allows to test utility of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.133-152
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• 2022

Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5^th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2^nd largest crop being produced and 3^rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.