• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.2
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• pp.205-213
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• 2018

Development and validation of crop models often require measurements of biomass for the crop of interest. Considerable efforts would be needed to obtain a reasonable amount of biomass data because the destructive sampling of a given crop is usually used. The Kinect sensor, which has a combination of image and depth sensors, can be used for estimating crop biomass without using destructive sampling approach. This approach could provide more data sets for model development and validation. The objective of this study was to examine the applicability of the Kinect sensor for estimation of chinese cabbage fresh weight. The fresh weight of five chinese cabbage was measured and compared with estimates using the Kinect sensor. The estimates were obtained by scanning individual chinese cabbage to create point cloud, removing noise, and building a three dimensional model with a set of free software. It was found that the 3D model created using the Kinect sensor explained about 98.7% of variation in fresh weight of chinese cabbage. Furthermore, the correlation coefficient between estimates and measurements were highly significant, which suggested that the Kinect sensor would be applicable to estimation of fresh weight for chinese cabbage. Our results demonstrated that a depth sensor allows for a non-destructive sampling approach, which enables to collect observation data for crop fresh weight over time. This would help development and validation of a crop model using a large number of reliable data sets, which merits further studies on application of various depth sensors to crop dry weight measurements.

• Journal of Preventive Medicine and Public Health
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• v.24 no.1 s.33
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• pp.93-97
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• 1991

The time honoured tuning fork at present widely available for examining vibration sensation is brought about the problem of great interobserver variation. To resolve t his problem, author developed a divice using electric magnet that stimulates constantly tuning fork. The perception time of vibration from tunung fork by this device was tested on the index finger of dominant hand of twenty eight subjects. It was 12.44 seconds on average and ranged from 9.47 to 17.25. Coefficient of variation of it was 16.89 percent. Correlation coefficient between test and retest after 30 minutes was 0.957(p<0.01). This device is portable. Test procedure in non-invasive, non-aversive and simple, can be performed within one minute, and does not require the skilled technician. It is felt that this device testing vibration perception time is suitable as screening tool for early detection of occupational peripheral neuropathy.

• Tunnel and Underground Space
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• v.30 no.3
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• pp.214-225
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• 2020

Uniaxial compressive strength (UCS) of rock is one of the important factors to determine the advance speed during shield TBM tunnel excavation. UCS can be obtained through the Geotechnical Data Report (GDR), and it is difficult to measure UCS for all tunneling alignment. Therefore, the purpose of this study is to predict UCS by utilizing TBM machine driving data and machine learning technique. Several machine learning techniques were compared to predict UCS, and it was confirmed the stacking model has the most successful prediction performance. TBM machine data and UCS used in the analysis were obtained from the excavation of rock strata with slurry shield TBMs. The data were divided into 8:2 for training and test and pre-processed including feature selection, scaling, and outlier removal. After completing the hyper-parameter tuning, the stacking model was evaluated with the root-mean-square error (RMSE) and the determination coefficient (R²), and it was found to be 5.556 and 0.943, respectively. Based on the results, the sacking models are considered useful in predicting rock strength with TBM excavation data.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.159-166
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• 2009

The purpose of this study was to develop and automatic gait event detection algorithm using single accelerometer which is attached at the top of the shoe. The sinal vector magnitude and anterior-posterior(x-axis) directional component of accelerometer were used to detect heel strike(HS) and toe off(TO), respectively. To evaluate proposed algorithm, gait event timing was compared with that by force plate and kinematic data. In experiment, 7 subjects performed 10 trials level walking with 3 different walking conditions such as fast, preferred & slow walking. An accelerometer, force plate and 3D motion capture system were used during experiment. Gait event by force plate was used as reference timing. Results showed that gait event by accelerometer is similar to that by force plate. The distribution of differences were spread about $22.33{\pm}17.45m$ for HS and $26.82{\pm}14.78m$ for To and most error was existed consistently prior to 20ms. The difference between gait event by kinematic data and developed algorithm was small. Thus it can be concluded that developed algorithm can be used during outdoor walking experiment. Further study is necessary to extract gait spatial variables by removing gravity factor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.211-221
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• 2012

To utilize omni-directional images acquired by a rotating line camera for indoor spatial information services, we should register precisely the images with respect to an indoor coordinate system. In this study, we thus develop a georeferencing method to estimate the exterior orientation parameters of an omni-directional image - the position and attitude of the camera at the acquisition time. First, we derive the collinearity equations for the omni-directional image by geometrically modeling the rotating line camera. We then estimate the exterior orientation parameters using the collinearity equations with indoor control points. The experimental results from the application to real data indicate that the exterior orientation parameters is estimated with the precision of 1.4 mm and $0.05^{\circ}$ for the position and attitude, respectively. The residuals are within 3 and 10 pixels in horizontal and vertical directions, respectively. Particularly, the residuals in the vertical direction retain systematic errors mainly due to the lens distortion, which should be eliminated through a camera calibration process. Using omni-directional images georeferenced precisely with the proposed method, we can generate high resolution indoor 3D models and sophisticated augmented reality services based on the models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.217-224
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• 2010

This study was carried out to quantify potential effects of the surrounding ocean on the observed air temperature at coastal weather stations in the Korean Peninsula. Daily maximum and minimum temperature data for 2001-2009 were collected from 66 Korea Meteorological Administration (KMA) stations and the monthly averages were calculated for further analyses. Monthly data from 27 inland sites were used to generate a gridded temperature surface for the whole Peninsula based on an inverse distance weighting and the local temperature at the remaining 39 sites were estimated by recent techniques in geospatial climatology which are widely used in correction of small - scale climate controls like cold air drainage, urban heat island, topography as well as elevation. Deviations from the observed temperature were regarded as the 'apparent' sea effect and showed a quasi-logarithmic relationship with the distance of each site from the nearest coastline. Potential effects of the sea on daily temperature might exceed $6.0^{\circ}C$ cooling in summer and $6.5^{\circ}C$ warming in winter according to this relationship. We classified 25 sites within the 10 km distance from the nearest coastline into 'coastal sites' and the remaining 15 'fringe sites'. When the average deviations of the fringe sites ($0.5^{\circ}C$ for daily maximum and $1.0^{\circ}C$ for daily minimum temperature) were used as the 'noise' and subtracted from the 'apparent' sea effects of the coastal sites, maximum cooling effects of the sea were identified as $1.5^{\circ}C$ on the west coast and $3.0^{\circ}C$ on the east and the south coast in summer months. The warming effects of the sea in winter ranged from $1.0^{\circ}C$ on the west and $3.5^{\circ}C$ on the south and east coasts.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.85-92
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• 2008

National Oceanographic Research Institute is carrying out an oceanographic survey for the entire sea areas around Korean Peninsula annually starting with the East Sea from 1996 by establishing a national oceanographic basic map survey plan for the sea areas under the jurisdiction of Korea, so this paper used the oceanographic geomagnetism data measured at the southern area of the Yellow Sea using 'Hae Yang 2000' in 1999, aiming at clarifying the cause of geomagnetic abnormality zone during the course of treating and analyzing the geomagnetic data. For treatment of magnetic data, we obtained electromagnetic force values and geomagnetic abnormality values around the investigated sea area through a process of searching and removal of bad data, correction of sensor positions, correction of magnetic field effects around the hull, correction of diurnal variation, normal correction, correction of cross point errors, etc. The electromagnetic force distribution around the investigated sea area was $49000\;{\sim}\;51600\;nT$, which is judged to be within the normal electromagnetic force intensity distribution range around the Yellow Sea. The isodynamic lines are distributed in Northeast-Southwest direction, and electromagnetic force values are increasing toward the northwest. The result of comparing the magnetic abnormality around the sea area among $124^{\circ}$ 49' 48" E, $35^{\circ}$ 10' 48" N $\sim$ $125^{\circ}$ 7' 48" E, and $35^{\circ}$ 33' 00" N sections with the elastic wave cross section and the result of modeling coincide well with the underground geological structure clarified from the existing elastic wave survey cross section. Therefore, it is judged that the distribution of magnetic force abnormality generally shows the effect pursuant to the distribution of the sedimentary basins in the Tertiary period and the bedrocks in the Cretaceous period which are well developed in the bottom of the sea.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.11-18
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• 2016

Since GNSS signals get blocked by buildings in urban canyons or narrow alleys, it is very difficult to secure a enough number of visible satellites for satellite navigation in those poor signal-reception environments. In those situations, one cannot get their coordinates or obtain accurate positions. In this study, a couple of strategies for improving positioning accuracy in urban canyons were developed and their performance was verified. First of all, we combined GPS and GLONASS measurements together and devised algorithms to quality-control observed signals and eliminate outliers. Also, a new multipath reduction scheme was applied to minimize its effect by utilizing SNR values of the observed signals. For performance verification of the developed technique, a narrow alley of 10m width located near the back gate of the Inha University was selected as the test-bed, and then we conducted static and kinematic positioning at four pre-surveyed points. We found that our new algorithms produced an 45% improvement in an open-sky environment compared with the positioning result of a low-cost u-blox receiver. In the alleys, 3-D accuracy improved by an average of 37%. In the case of kinematic positioning, especially, biases showing up in regular receivers got eliminated significantly through our new filtering algorithms.