• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.123-135
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• 2013

The reflectance observed in the visible channels of a geostationary meteorological satellite can be used to calculate the amount of cloud by comparing the reflectance with the observed solar radiation data at the ground. Using this, the solar radiation arriving at the surface can be estimated. This study used the Meteorological Imager (MI) reflectance observed at a wavelength of 675 nm and the Geostationary Ocean Color Imager (GOCI) reflectance observed at similar wavelengths of 660 and 680 nm. Cloudy days during a typhoon and sunny days with little cloud cover were compared using observation data from the geostationary satellite. Pixels that had more than 40% reflectance in the satellite images showed less than 0.3 of the cloud index and blocked more than 70% of the solar energy. Pixels that showed less than 15% reflectance showed more than 0.9 of the cloud index and let through more than 90% of the solar energy to the surface. The calculated daily accumulated solar radiation was compared with the observed daily accumulated solar radiation in 22 observatories of the Korean Meteorological Administration. The values calculated for the COMS and MTSAT MI sensors were smaller than the observation and showed low correlations of 0.94 and 0.93, respectively, which were smaller than the 0.96 correlation coefficient calculated for the GOCI sensor. The RMSEs of MTSAT, COMS MI and GOCI calculation results showed 2.21, 2.09, 2.02 MJ/$m^2$ in order. Comparison of the calculated daily accumulated results from the GOCI sensor with the observed data on the ground gave correlations and RMSEs for cloudy and sunny days of 0.96 and 0.86, and 1.82 MJ/$m^2$ and 2.27 MJ/$m^2$, respectively, indicating a slightly higher correlation for cloudy days. Compared to the meteorological imager, the geostationary ocean color imager in the COMS satellite has limited observation time and observation is not continuous. However, it has the advantage of providing high resolution so that it too can be useful for solar energy analysis.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Bio-Environment Control
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• v.25 no.1
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• pp.63-70
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• 2016

Water drainage from the open hydroponics often causes significant environmental pollution due to agrochemicals and loss of water and nutrients. The objectives of this study were to show the potential application of an irrigation schedule based on threshold values of volumetric substrate water content for tomato (Solanum lycopersicum L. 'Samsamgu') cultivation in a commercial hydroponic farm during spring to summer cultivation. This study was performed for minimizing effluent from coir substrate hydroponics using a frequency domain reflectometry (FDR) sensor-automated irrigation, as compared with an integrated solar-radiation (IR) and conventional timer-irrigation (TIMER) after transplanting. In results, no significant difference in daily irrigation volume was found among the treatments until 88 days after transplant (DAT). However, during the 88 to 107 DAT, the daily irrigation volume was in the order of IR (2125 mL) > TIMER (2063 mL) > FDR (1983 mL), and during the 108 to 120 DAT, it was in the order of IR (2000 mL) > TIMER (1664 mL) > FDR (1500 mL). The lowest drainage volume was observed in the FDR treatment with the order of IR (12~19%) > TIMER (4~12%) > FDR (0~7%) during the entire growing period. A lower irrigation volume in the FDR treatment after 88 DAT may be due to the sensor's detecting capacity for less water absorption by plant after completing fruit maturity with apical pruning and removal of lower leaves, while a higher irrigation volume in the IR treatment may be due to gradual increase in integrated solar-radiation amount as closer to summer season. There was no significant difference in plant growth and fruit yield among the treatments; however, a 11% and 18% of higher soluble sugar content was observed in the FDR than that of TIMER and IR treatment. respectively.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.102-106
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• 2012

The purpose of this paper is to study the failure cases in relation to system of Air Bag in vehicle happened in the field. In the first example, it was separated the soldering parts connected the wire pin between air bag module and clock spring of air bag. Whenever the pin shake by the car's vibration, the driver verified the malfunction phenomenon appeared air bag warning lamp on instrument panel in front of driver's seat. in car inside room. The second example, it verified the warning lamp lighting phenomenon of air bag by produced the circuit plate non-contacting of single an element in air bag electronic control unit. The third example, it verified the light of air bag warning indicator lamp by separated with soldering parts connecting inner pin and resistance terminal of seat belt pretensioner using passenger seat. The fourth example, when the passenger car crash a back of truck, the former bumper get jammed under the latter as the roof height of car low less than that. Therefore, the impact of Car's collision verified that don't transfer with body frame of vehicle because of no attachment impact sensor in it.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.377-385
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• 2017

The solar cell need the characteristic interpreting because the solar cell changes greatly according to the isolation, temperature and load in the photovoltaic development. Moreover, to get many energy in photovoltaic development need the position tracking of the sun according to the environment change. Also, The solar cells should be operated at the maximum power point. In this paper, I used microprocessor and sensor and designed to improve the efficiency of the photovoltaic system the photovoltaic position tracker device, and compared the normal photovoltaic system of fixed form with the photovoltaic system of solar position tracked form. Moreover, compared the catalogue of solar cell module and the simulation through a mathematics modelling with the solar cell's characteristic interpreting and composed an power conversion system with boost converter and voltage source inverter. Used the constant voltage control method for maximum power point tracking in boost converter control and, used the SPWM(Sinusoidal Pulse Width Modulation) control method in inverter control. The result was less then 5% when compared the catalogue of solar cell module and the simulation through a mathematics modelling. The boost rate of boost converter was similar to 167 % with the simulation.

• Journal of Ecology and Environment
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• v.41 no.3
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• pp.45-53
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• 2017

Background: There have been many studies on the growth conditions of Zostera marina and Zostera japonica, but few studies have examined how spatial and temporal factors affect growth in established seagrass beds or the distribution range and shoot density. This study aims to clarify the factors that determine the temporal and spatial distribution of Zostera marina and Zostera japonica in the Seto Inland Sea east of Yamaguchi Prefecture. Methods: The study site is in Hiroshima Bay of the Seto Inland Sea, along the east coast of Yamaguchi Prefecture, Japan. We monitored by diving observation to confirm shoot density, presence or absence of both species and observed water temperature, salinity by sensor in study sites. Results: The frequency of occurrence of Zostera marina was high in all seasons, even in water depths of D.L. + 1 to -5 m ($80{\pm}34%$ to $89{\pm}19%$; mean ${\pm}$ standard deviation), but lower (as low as $43{\pm}34%$) near the breakwall, where datum level was 1 to 2 m, and it was further reduced in datum level -5 m and deeper. The frequency of occurrence of Zostera japonica was highest in water with a datum level of +1 to 0 m. However, in datum level of 0 m or deeper, it became lower as the water depth became deeper. Datum level +1 m to 0 m was an optimal water depth for both species. The frequency of occurrence and the shoot density of both species showed no negative correlation. In 2011, the daily mean water temperature was $10^{\circ}C$ or less on more days than in other years and the feeding damage by S. fuscescens in the study sites caused damage at the tips. Conclusions: We considered that the relationship between these species at the optimal water depth was not competitive, but due to differences in spatial distribution, Zostera marina and Zostera japonica do not influence each other due to temperature conditions and feeding damage and other environmental conditions. Zostera japonica required light intensity than Zostera marina, and the water depth played an important role in the distribution of both species.

• Journal of the Korean Applied Science and Technology
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• v.34 no.2
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• pp.394-399
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• 2017

The goal of this study is to suggest the technology and the way of developing ergonomic shampoo device, which is able to adjust the height and to be devided, and it uses ozone water. As a result of developing the device, it can complete better the effect of preventing water splash than existing devices by making neck holding part higher. And it is also made with ergonomic design, therefore, the head of shampoo candidate can be drawn into it more easily. By adjusting water temperature($38^{\circ}C$) to candidate's taste through water heater attached to water bucket, when a candidate is being shampooed, it can help keep warm shampooing without delaying. We could know the process through temperature sensor. And we could also know the utility of its own sterilization(1PPM) and purification. Finally, ozone water was measured for 20 minutes and the ozone concentration was measured to be less than 1 PPM to ensure stability. All parts of the mobile shampoo stand together with the hot water device and the ozonated water conversion device were designed so as not to be inconvenient for the user to use.

• Advances in aircraft and spacecraft science
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• v.5 no.2
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• pp.225-237
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• 2018

The difficulties of satellite vibration testing are due to the commonly expressed qualification requirements being incompatible with the limited performance of the entire controlled system (satellite + interface + shaker + controller). Two features cause the problem: firstly, the main satellite modes (i.e., the first structural mode and the high and low tank modes) are very weakly damped; secondly, the controller is just too basic to achieve the expected performance in such cases. The combination of these two issues results in oscillations around the notching levels and high amplitude beating immediately after the mode. The beating overshoots are a major risk source because they can result in the test being aborted if the qualification upper limit is exceeded. Although the abort is, in itself, a safety measure protecting the tested satellite, it increases the risk of structural fatigue, firstly because the abort threshold has been already reached, and secondly, because the test must restart at the same close-resonance frequency and remain there until the qualification level is reached and the sweep frequency can continue. The beat minimum relates only to small successive frequency ranges in which the qualification level is not reached. Although they are less problematic because they do not cause an inadvertent test shutdown, such situations inevitably result in waiver requests from the client. A controlled-system analysis indicates an operating principle that cannot provide sufficient stability: the drive calculation (which controls the process) simply multiplies the frequency reference (usually called cola) and a function of the following setpoint, the ratio between the amplitude already reached and the previous setpoint, and the compression factor. This function value changes at each cola interval, but it never takes into account the sensor signal phase. Because of these limitations, we firstly examined whether it was possible to empirically determine, using a series of tests with a very simple dummy, a controller setting process that significantly improves the results. As the attempt failed, we have performed simulations seeking an optimum adjustment by finding the Least Mean Square of the difference between the reference and response signal. The simulations showed a significant improvement during the notch beat and a small reduction in the beat amplitude. However, the small improvement in this process was not useful because it highlighted the need to change the reference at each cola interval, sometimes with instructions almost twice the qualification level. Another uncertainty regarding the consequences of such an approach involves the impact of differences between the estimated model (used in the simulation) and the actual system. As limitations in the current controller were identified in different approaches, we considered the feasibility of a new controller that takes into account an estimated single-input multi-output (SIMO) model. Its parameters were estimated from a very low-level throughput. Against this backdrop, we analyzed the feasibility of an LQG control in cancelling beating, and this article highlights the relevance of such an approach.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.481-489
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• 2019

Change detection, one of the main applications of multi-temporal satellite images, is an indicator that directly reflects changes in human activity. Change detection can be divided into pixel-based change detection and object-based change detection. Although pixel-based change detection is traditional method which is mostly used because of its simple algorithms and relatively easy quantitative analysis, applying this method in VHR (Very High Resolution) images cause misdetection or noise. Because of this, pixel-based change detection is less utilized in VHR images. In addition, the sensor of acquisition or geographical characteristics bring registration noise even if co-registration is conducted. Registration noise is a barrier that reduces accuracy when extracting spatial information for utilizing VHR images. In this study object-based change detection of VHR images was performed considering registration noise. In this case, object-based change detection results were derived considering various pixel-based change detection methods, and the major voting technique was applied in the process with segmentation image. The final object-based change detection result applied by the proposed method was compared its performance with other results through reference data.