• Journal of the Korean Wood Science and Technology
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• v.39 no.4
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• pp.318-325
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• 2011

Pitch pine (Pinus rigida Miller) retaining walls using Steel bar, of which the constructability and strength performance are good at the construction site, were manufactured and their strength properties were evaluated. The wooden retaining wall using Steel bar was piled into four stories stretcher and three stories header, which is 770 mm high, 2,890 mm length and 782 mm width. Retaining wall was made by inserting stretchers into Steel bar after making 18 mm diameter of holes at top and bottom stretcher, and then stacking other stretchers and headers which have a slit of 66 mm depth and 18 mm width. The strength properties of retaining walls were investigated by horizontal loading test, and the deformation of structure by image processing (AlCON 3D OPA-PRO system). Joint (Type-A) made with a single long stretcher and two headers, and joint (Type-B) made with two short stretchers connected with half lap joint and two headers were in the retaining wall using Steel bar. The compressive shear strength of joint was tested. Three replicates were used in each test. In horizontal loading test the strength was 1.6 times stronger in wooden retaining wall using Steel bar than in wooden retaining wall using square timber. The timber and joints were not fractured in the test. When testing compressive shear strength, the maximum load of type-A and Type-B was 130.13 kN and 130.6 kN, respectively. Constructability and strength were better in the wooden retaining wall using Steel bar than in wooden retaining wall using square timber.

• Journal of the Korean Wood Science and Technology
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• v.36 no.6
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• pp.77-87
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• 2008

Wooden model erosion control dam was made with pitch pine, of which the strength properties was evaluated. Wooden model erosion control dam was made with diameter 90 mm of pitch pine round posts treated with CUAZ-2 (Copper Azole), changing joint in three different types. In each type, erosion control dam was made in nine floor (cross-bar of five floors and vertical-bar of four floors), of which the hight was 790 mm. And then strength properties were investigated through horizontal loading test and impact strength test, and the deformation of structure through image processing (AICON 3D DPA-PRO system). In horizontal loading test of wooden model erosion control dam using round post of diameter 90 mm, whether there was stone or not did not affect strength much when using self drill screw, but strength was decreased by 23%. In monolithic type of erosion control dam using screw bar, strength was increased by 1.5 times and deformation was decreased when filling with stone. When reinforcing with screw bar that ring is connected to self drill screw, strength was increased by 4.8 times. In impact strength test of wooden model erosion control dam made with round post of diameter 90 mm, the erosion control dam connected with self drilling screw not filling with stone was totally destroyed by the 1st impact, and the erosion control dam using screw bar was ruptured at cross-bar at which 779 kgf of impact was loaded in the 1st impact. In the 2nd impact, the base parts were ruptured, and reaction force was decreased to 545 kgf. In the 3rd impact, whole base parts were destroyed, and reaction force was decreased to 263 kgf.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.33-43
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• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.7
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• pp.586-603
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• 2004

One of the subtle problems that make noise control difficult for engineers is “the invisibility of noise or sound.” The visual image of noise often helps to determine an appropriate means for noise control. There have been many attempts to fulfill this rather challenging objective. Theoretical or numerical means to visualize the sound field have been attempted and as a result, a great deal of progress has been accomplished, for example in the field of visualization of turbulent noise. However, most of the numerical methods are not quite ready to be applied practically to noise control issues. In the meantime, fast progress has made it possible instrumentally by using multiple microphones and fast signal processing systems, although these systems are not perfect but are useful. The state of the art system is recently available but still has many problematic issues : for example, how we can implement the visualized noise field. The constructed noise or sound picture always consists of bias and random errors, and consequently it is often difficult to determine the origin of the noise and the spatial shape of noise, as highlighted in the title. The first part of this paper introduces a brief history, which is associated with “sound visualization,” from Leonardo da Vinci's famous drawing on vortex street (Fig. 1) to modern acoustic holography and what has been accomplished by a line or surface array. The second part introduces the difficulties and the recent studies. These include de-Dopplerization and do-reverberation methods. The former is essential for visualizing a moving noise source, such as cars or trains. The latter relates to what produces noise in a room or closed space. Another mar issue associated this sound/noise visualization is whether or not Ivecan distinguish mutual dependence of noise in space : for example, we are asked to answer the question, “Can we see two birds singing or one bird with two beaks?"

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.384-392
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• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.