• Food Science of Animal Resources
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• v.43 no.6
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• pp.1150-1169
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• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.3
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• pp.121-128
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• 2024

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f^0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune's source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.13-27
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• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.3
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• pp.195-205
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• 2020

This study addresses analysis on reactions which are induced in restraint system for airframe full-scale static structural test. This system restraints 6 degrees of freedom of a test article. It is valuable to study evaluating test error through analysis on the reactions which include all errors in a test. It is required to calculate fistly right reactions for the evaluation. This study focuses on calculation of the right reactions. The reaction is represented by sum of nominal reaction(Rn) and testing error reactions(Rce, Rerr) and is analyzed by two steps (inital vs relative reaction) in this study. It would evaluate intrinsic error at 0%DLL and error induced from applying test load, separately. Based on analysis using test data of a full-scale static test(canard type aircraft), resultant force of Rces and Rce_rs are distributed within 82.8N while resultant force of Rerr_rs shows to increase upto max. 808N as load level increment. Such well distribution of the Rce within the small range is caused from TMF values characteristics which are well distributed within -30N~40N. Additionally, it is shown through qualitative analysis on three components(X0, Y0, Z0) of the relative reaction(Rerr_r) that the reactions must be calculated with considering deformation of test article to calculate correctly reactions. This study shows also that equations characterizing deformation of components of test article are required to calculate the correct reactions, the equations must include information which will be used to calculate movement of all loading points.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.467-476
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• 2013

Site index information given from forest site map only exist in the sampled locations. In this study, site index for unsampled locations were estimated using kriging interpolation method which can interpolate values between point samples to generate a continuous surface. Site index of Pinus densiplora in Danyang area were calculated using Chapman-Richards model by plot unit. Then site index for unsampled locations were interpolated by theoretical variogram models and ordinary kriging. Also in order to assess parameter selection, cross-validation was performed by calculating mean error (ME), average standard error (ASE) and root mean square error (RMSE). In result, gaussian model was excluded because of the biggest relative nugget (37.40%). Then spherical model (16.80%) and exponential model (8.77%) were selected. Site index estimates of Pinus densiplora throughout the entire area in Danyang showed 4.39~19.53 based on exponential model, and 4.54~19.23 based on spherical model. By cross-validation, RMSE had almost no difference. But ME and ASE from spherical model were slightly lower than exponential model. Therefore site index prediction map from spherical model were finally selected. Average site index from site prediction map was 10.78. It can be expected that regional variance can be considered by site index prediction map in order to estimate forest biomass which has big spatial variance and eventually it is helpful to improve an accuracy of forest carbon estimation.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.1
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• pp.55-63
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• 2016

Temperature lapse rate within the planetary boundary layer shows a diurnal cycle with a substantial variation. The widely-used lapse rate value for the standard atmosphere may result in unaffordable errors if used in interpolating hourly temperature in complex terrain. We propose a simple method for estimating hourly lapse rate and evaluate whether this scheme is better than the conventional method using the standard lapse rate. A standard curve for lapse rate based on the diurnal course of temperature was drawn using upper air temperature for 1000hPa and 925hPa standard pressure levels. It was modulated by the hourly sky condition (amount of clouds). In order to test the reliability of this method, hourly lapse rates for the 500-600m layer over Daegwallyeong site were estimated by this method and compared with the measured values by an ultrasonic temperature profiler. Results showed the mean error $-0.0001^{\circ}C/m$ and the root mean square error $0.0024^{\circ}C/m$ for this vertical profile experiment. An additional experiment was carried out to test if this method is applicable for the mountain slope lapse rate. Hourly lapse rates for the 313-401m slope range in a complex watershed ('Hadong Watermark 2') were estimated by this method and compared with the observations. We found this method useful in describing diurnal cycle and variation of the mountain slope lapse rate over a complex terrain despite larger error compared with the vertical profile experiment.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.61-71
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• 2017

The tracer gas method has an advantage that can estimate total and local ventilation rate by tracing air flow. However, the field measurement using tracer gas has disadvantages such as danger, inefficiency, and high cost. Therefore, the aim of this study was to evaluate ventilation rate in pig house by using the thermal distribution data rather than tracer gas. Especially, LMA (Local Mean Age), which is an index based on the age of air theory, was used to evaluate the ventilation rate in pig house. Firstly, the field experiment was conducted to measure micro-climate inside pig house, such as the air temperature, $CO_2$ concentration and wind velocity. And then, LMA was calculated based on the decay of $CO_2$ concentration and air temperature, respectively. This study compared between LMA determined by $CO_2$ concentration and air temperature; the average error and root mean square error were 3.76 s and 5.34 s. From these results, it was determined that thermal distribution data could be used for estimation of LMA. Finally, CFD (Computational fluid dynamic) model was validated using LMA and wind velocity. The mesh size was designed to be 0.1 m based on the grid independence test, and the Standard $k-{\omega}$ model was eventually chosen as the proper turbulence model. The developed CFD model was highly appropriate for evaluating the ventilation rate in pig house.