• Title/Summary/Keyword: error performance

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The Advanced Bias Correction Method based on Quantile Mapping for Long-Range Ensemble Climate Prediction for Improved Applicability in the Agriculture Field (농업적 활용성 제고를 위한 분위사상법 기반의 앙상블 장기기후예측자료 보정방법 개선연구)

  • Jo, Sera;Lee, Joonlee;Shim, Kyo Moon;Ahn, Joong-Bae;Hur, Jina;Kim, Yong Seok;Choi, Won Jun;Kang, Mingu
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.24 no.3
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    • pp.155-163
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    • 2022
  • The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

A Comparison of Analysis Methods for Work Environment Measurement Databases Including Left-censored Data (불검출 자료를 포함한 작업환경측정 자료의 분석 방법 비교)

  • Park, Ju-Hyun;Choi, Sangjun;Koh, Dong-Hee;Park, Donguk;Sung, Yeji
    • Journal of Korean Society of Occupational and Environmental Hygiene
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    • v.32 no.1
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    • pp.21-30
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    • 2022
  • Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

Image Matching for Orthophotos by Using HRNet Model (HRNet 모델을 이용한 항공정사영상간 영상 매칭)

  • Seong, Seonkyeong;Choi, Jaewan
    • Korean Journal of Remote Sensing
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    • v.38 no.5_1
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    • pp.597-608
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    • 2022
  • Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

A Study on the Compensation Methods of Object Recognition Errors for Using Intelligent Recognition Model in Sports Games (스포츠 경기에서 지능인식모델을 이용하기 위한 대상체 인식오류 보상방법에 관한 연구)

  • Han, Junsu;Kim, Jongwon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.5
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    • pp.537-542
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    • 2021
  • This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

Estimation and Evaluation of Reanalysis Air Temperature based on Mountain Meteorological Observation (산악기상정보 융합 기반 재분석 기온 데이터의 추정 및 검증)

  • Sunghyun, Min;Sukhee, Yoon;Myongsoo, Won;Junghwa, Chun;Keunchang, Jang
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.24 no.4
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    • pp.244-255
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    • 2022
  • This study estimated and evaluated the high resolution (1km) gridded mountain meteorology data of daily mean, maximum and minimum temperature based on ASOS (Automated Surface Observing System), AWS (Automatic Weather Stations) and AMOS (Automatic Mountain Meteorology Observation System) in South Korea. The ASOS, AWS, and AMOS meteorology data which were located above 200m was classified as mountainous area. And the ASOS, AWS, and AMOS meteorology data which were located under 200m was classified as non-mountainous area. The bias-correction method was used for correct air temperature over complex mountainous area and the performance of enhanced daily coefficients based on the AMOS and mountainous area observing meteorology data was evaluated using the observed daily mean, maximum and minimum temperature. As a result, the evaluation results show that RMSE (Root Mean Square Error) of air temperature using the enhanced coefficients based on the mountainous area observed meteorology data is smaller as 30% (mean), 50% (minimum), and 37% (maximum) than that of using non-mountainous area observed meteorology data. It indicates that the enhanced weather coefficients based on the AMOS and mountain ASOS can estimate mean, maximum, and minimum temperature data reasonably and the temperature results can provide useful input data on several climatological and forest disaster prediction studies.

Experimental Study on the Proposal of an Assessment Method and Quality Standard for Identifying the Fine Particles of Clay Components in Fine Aggregates (잔골재의 토분 평가방법 및 품질기준 제안을 위한 실험적 연구)

  • Choi, Hyun-Kyu;Han, Min-Cheol
    • Journal of the Korea Institute of Building Construction
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    • v.22 no.6
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    • pp.585-596
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    • 2022
  • The purpose of this study is to propose an assessment method to analyze clay collectively referred to as fine particles of clay components contained in fine aggregates, and to propose quality standards for clay use through correlation with the performance of concrete to verify the properties of clay measured according to the method. As a result, it is analyzed that it will be suitably utilized as a method to assess the fine particles of the clay component of fine aggregates through the component analysis of XRF. Regarding the related quality standards, considering the error rate of about 10% of KCS 14 20 10, the related quality standards were analyzed to be safe when Al2O3+Fe2O3+MgO is 23.5% or less and SiO2+K2OSiO2+K22O is 66.5% or more. To build on this study, it is expected that a comprehensive review will be conducted through additional follow-up studies such as on clay of coarse aggregates and durability analysis to establish a system for quality control of the soil fraction of aggregates.

Burn-back Analysis for Propellant Grains with Embedded Metal Wires (금속선이 삽입된 추진제 그레인의 Burn-back 해석)

  • Lee, Hyunseob;Oh, Jongyun;Yang, Heesung;Lee, Sunyoung;Khil, Taeock
    • Journal of the Korean Society of Propulsion Engineers
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    • v.26 no.2
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    • pp.12-19
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    • 2022
  • Propellant grains with embedded metal wires have been used for enhancement of burning rate while maintaining high loading density. For the performance design of a solid rocket motor using propellant grain with embedded metal wires, burn-back analysis is required according to number, location, arrangement angle of metal wires, and augmentation ratio of the propellant burning rate near a wire region. In this study, a numerical method to quickly calculate a burning surface area was developed in response to the design change of the propellant grain with embedded metal wires. The burning surface area derived from the developed method was compared with the results of a CAD program. Error rate decreased as the radial size of the grid decreased. Analysis for characteristics of burning surface area was performed according to the number and location of metal wires, the initial and final phases were shortened and the steady-state phase was increased when the number of metal wires increased. When arranging the metal wires at different radii, the burning surface area rapidly increased in the initial phase and sharply decreased in the final phase compared to the case where the metal wires were disposed in the same radius.

Determination of Survival of Gastric Cancer Patients With Distant Lymph Node Metastasis Using Prealbumin Level and Prothrombin Time: Contour Plots Based on Random Survival Forest Algorithm on High-Dimensionality Clinical and Laboratory Datasets

  • Zhang, Cheng;Xie, Minmin;Zhang, Yi;Zhang, Xiaopeng;Feng, Chong;Wu, Zhijun;Feng, Ying;Yang, Yahui;Xu, Hui;Ma, Tai
    • Journal of Gastric Cancer
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    • v.22 no.2
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    • pp.120-134
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    • 2022
  • Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

THE USE OF NEAR INFRARED REFLECTANCE SPECTROSCOPY(NIRS) TO PREDICT CHEMICAL COMPOSITION ON MAIZE SILAGE

  • D.Cozzolino;Fassio, A.;Mieres, J.;Y.Acosta
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1610-1610
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    • 2001
  • Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

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Study of Improved CNN Algorithm for Object Classification Machine Learning of Simple High Resolution Image (고해상도 단순 이미지의 객체 분류 학습모델 구현을 위한 개선된 CNN 알고리즘 연구)

  • Hyeopgeon Lee;Young-Woon Kim
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.1
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    • pp.41-49
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    • 2023
  • A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.