• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.155-170
• /
• 2020

Passive ranging is a critical part of machine vision measurement. Most of passive ranging methods based on machine vision use binocular technology which need strict hardware conditions and lack of universality. To measure the distance of an object placed on horizontal plane, we present a passive ranging method based on monocular vision system by smartphone. Experimental results show that given the same abscissas, the ordinatesis of the image points linearly related to their actual imaging angles. According to this principle, we first establish a depth extraction model by assuming a linear function and substituting the actual imaging angles and ordinates of the special conjugate points into the linear function. The vertical distance of the target object to the optical axis is then calculated according to imaging principle of camera, and the passive ranging can be derived by depth and vertical distance to the optical axis of target object. Experimental results show that ranging by this method has a higher accuracy compare with others based on binocular vision system. The mean relative error of the depth measurement is 0.937% when the distance is within 3 m. When it is 3-10 m, the mean relative error is 1.71%. Compared with other methods based on monocular vision system, the method does not need to calibrate before ranging and avoids the error caused by data fitting.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.457-469
• /
• 2022

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

• Current Optics and Photonics
• /
• v.2 no.2
• /
• pp.119-124
• /
• 2018

The errors in retrieved aerosol backscattering coefficients due to different lidar ratios are analyzed quantitatively in this paper. The actual calculation shows that the inversion error of the aerosol backscattering coefficients using the Fernald backward-integration method increases with increasing inversion distance. The greater the error in the lidar ratio, the faster the error in the aerosol backscattering coefficient increases. For the same error in lidar ratio, the smaller actual aerosol backscattering coefficient will get the larger relative error of the retrieved aerosol backscattering coefficient. The errors in the lidar ratios for dust or the cirrus layer have great impact on the retrievals of backscattering coefficients. The interval between the retrieved height and the reference range is one of the important factors for the derived error in the aerosol backscattering coefficient, which is revealed quantitatively for the first time in this paper. The conclusions of this article can provide a basis for error estimation in retrieved backscattering coefficients of background aerosols, dust and cirrus layer. The errors in the lidar ratio of an aerosol layer influence the retrievals of backscattering coefficients for the aerosol layer below it.

• Journal of the Korean Mathematical Society
• /
• v.33 no.1
• /
• pp.69-79
• /
• 1996

• Journal of the Korea Convergence Society
• /
• v.8 no.5
• /
• pp.185-192
• /
• 2017

Machine tool errors can be divided into geometric error, thermal deformation error, and machining error. In this study, the influence of each error on the total error and the relative size of each error are quantitatively analyzed in 2D machining. The thermal deformation error and the machining error caused a relatively large error compared to the geometric error, which is directly related to the machining accuracy. In order to eliminate the error factors, the possibility of error compensation was examined by analyzing the measured error profile shape. As a result, about 40 ~ 50% error compensation was achieved for each error factor. Through this study, it is possible to construct a basic data base on machining, and it is expected that it will be able to compensate the machining error from the viewpoint of users.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.10
• /
• pp.962-969
• /
• 2007

In this paper, an approach is developed for relative state estimation of satellite formation flying. To estimate relative states of two satellites, the Extended Kalman Filter Algorithm is adopted with the relative distance and speed between two satellites and attitude of satellite for measurements. Numerical simulations are conducted under two circumstances. The first one presents both chief and deputy satellites are orbiting a circular reference orbit around a perfectly spherical Earth model with no disturbing acceleration, in which the elementary relative orbital motion is taken into account. In reality, however, the Earth is not a perfect sphere, but rather an oblate spheroid, and both satellites are under the effect of $J_2$ geopotential disturbance, which causes the relative distance between two satellites to be on the gradual increase. A near-Earth orbit decays as a result of atmospheric drag. In order to remove the modeling error, the second scenario incorporates the effect of the $J_2$ geopotential force, and the atmospheric drag, and the eccentricity in satellite orbit are also considered.

• Atmosphere
• /
• v.19 no.2
• /
• pp.213-226
• /
• 2009

In order to investigate the characteristics of Changma over the Korean peninsula, KEOP-2007 IOP (Intensive Observing Period) was conducted from 15 June 2007 to 15 July 2007. KEOP-2007 IOP is high spatial and temporal radiosonde observations (RAOB) which consisted of three special stations (Munsan, Haenam, and Ieodo) from National Institute of Meteorological Research, five operational stations (Sokcho, Baengnyeongdo, Pohang, Heuksando, and Gosan) from Korea Meteorological Administration (KMA), and two operational stations (Osan and Gwangju) from Korean Air Force (KAF) using four different types of radiosonde sensors. The error statistics of the sensor of radiosonde were investigated using quality control check. The minimum and maximum error frequency appears at the sensor of RS92-SGP and RS1524L respectively. The error frequency of DFM-06 tends to increase below 200 hPa but RS80-15L and RS1524L show vice versa. Especially, the error frequency of RS1524L tends to increase rapidly over 200 hPa. Systematic biases of radiosonde show warm biases in case of temperature and dry biases in case of relative humidity compared with ECMWF (European Center for Medium-Range Weather Forecast) analysis data and precipitable water vapor from GPS. The maximum and minimum values of systematic bias appear at the sensor of DFM-06 and RS92-SGP in case of temperature and RS80-15L and DFM-06 in case of relative humidity. The systematic warm and dry biases at all sensors tend to increase during daytime than nighttime because air temperature around sensor increases from the solar heating during daytime. Systematic biases of radiosonde are affected by the sensor type and the height of the sun but random errors are more correlated with the moisture conditions at each observation station.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.5
• /
• pp.399-405
• /
• 2007

Providing a precise positioning information is the primary characteristics of GPS. The relative positioning technique which utilizes the common measurements between a GPS reference station and a user is generally used to do the generation of a precise positioning. But if user is far from a GPS reference site, the properties of medium penetrated by GPS signals will be different from each other, It is difficult to eliminate the error sources such as the ionosphere and the troposphere effectively by the double differencing method. In this study the additional error correction values with the ionosphere and the troposphere to the data processing have applied. As a result, the positioning accuracy of fourteen out of seventeen testing sites were improved by appling the error correction values. We also analysed the improved rate of the positioning accuracy by the baseline.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.27-36
• /
• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Geomechanics and Engineering
• /
• v.34 no.6
• /
• pp.697-726
• /
• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.