• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.1-9
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• 2012

As we are turnning into the aged society, accidents by falling down are increasing in the aged people's group. In this paper, we design the system with the 3-Axis acceleration sensor which is composed by a single chip. The body activity signal is measured with the signal detector and RF communicator in this proposed system and the and falling by the entering signal pattern analysis with 3-Axis acceleration sensor. For the RF communication, we are using nRF24L01p and 8bits ATmega uC for the processor. The error of energy expenditure estimation between motor driven treadmill and proposed a body activity module was 7.8% respectively. Human activities and falling is monitored according to analyze and judge the critical value of the Signal Vector. as falled down if they don't turn off the alarm after specific period and the aged person's after falling down activities are their position and more.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1075-1086
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• 2019

Recently, as the incidence of climate warming and abnormal climate increases, the forecasting of hydrological factors such as precipitation and river flow is getting more complicated, and the risk of water shortage is also increasing. Therefore, this study aims to develop a model for predicting the amount of water intake in mid-term. To this end, the correlation between water intake and meteorological factors, including temperature and precipitation, was used to select input factors. In addition, the amount of water intake increased with time series and seasonal characteristics were clearly shown. Thus, the preprocessing process was performed using the time series decomposition method, and the support vector machine (SVM) was applied to the residual to develop the river intake prediction model. This model has an error of 4.1% on average, which is higher accuracy than the SVM model without preprocessing. In particular, this model has an advantage in mid-term prediction for one to two months. It is expected that the water intake forecasting model developed in this study is useful to be applied for water allocation computation in the permission of river water use, water quality management, and drought measurement for sustainable and efficient management of water resources.

• Safety and Health at Work
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• v.10 no.4
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.337-348
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• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• Journal of Astronomy and Space Sciences
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• v.20 no.1
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• pp.21-28
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• 2003

In case that we independently obtain orbital informations about the low earth satellites of foreign countries using radar systems, we develop the orbit determination algorithm for this purpose using a SGP4 model with an analytical orbit model and the extended Kalman filter with a real-time processing method. When the state vector is Keplerian orbital elements, singularity problems happen to compute partial derivative with respect to inclination and eccentricity orbit elements. To cope with this problem, we set state vector osculating to mean equinox and true equator cartesian elements with coordinate transformation. The state transition matrix and the covariance matrix are numerically computed using a SGP4 model. Observational measurements are the type of azimuth, elevation and range, filter process to each measurement in a lump. After analyzing performance of the developed orbit determination algorithm using TOPEX/POSEIDON POE(precision 0.bit Ephemeris), its position error has about 1 km. To be similar to performance of NORAD system that has up to 3km position accuracy during 7 days need to radar system performance that have accuracy within 0.1 degree for azimuth and elevation and 50m for range.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.118-125
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• 2014

An approximation of speed of sound in the measurement plane is essential for the inverse estimation of temperature. To this end, an inverse problem relating the measured retarded time data in between set of sensors and actuators array located on the wall is formulated. The involved transfer matrix and its coefficient vectors approximate speed of sound of the measurement plane by using the radial basis function with finite number of interpolation points deployed inside the target field. Then, the temperature field can be reconstructed by using spatial interpolation technique, which can achieve high spatial resolution with proper number of interpolation points. A large number of retarded time data of acoustic paths in between sensors and arrays are needed to obtain accurate reconstruction result. However, the shortage of interpolation points due to practical limitations can cause the decrease of spatial resolution and deterioration of the reconstruction result. In this works, a regeneration for obtaining the additional retarded time data for an arbitrary acoustic path is suggested to overcome the shortage of interpolation points. By applying the regeneration technique, many interpolation points can be deployed inside the field by increasing the number of retarded time data. As a simulation example, two rectangular duct sections having arbitrary temperature distribution are reconstructed by two different data set: measured data only, combination of measured and regenerated data. The result shows a decrease in reconstruction error by 15 % by combining the original and regenerated retarded time data.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.