• 한국환경복원기술학회지
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• 제18권2호
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• pp.89-104
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• 2015

In order to effectively manage forested areas in South Korea on a national scale, using remotely sensed data is considered most suitable. In this study, utilizing Land coverage maps and Forest type maps of national geographic information instead of collecting field data was tested for conducting supervised classification on SPOT-5 and KOMPSAT-2 imagery focusing on forested areas. Supervised classification were conducted in two ways: analysing a whole area around the study site and/or only forested areas around the study site, using Support Vector Machine. The overall accuracy for the classification on the whole area ranged from 54.9% to 68.9% with kappa coefficients of over 0.4, which meant the supervised classification was in general considered moderate because of sub-classifying forested areas into three categories (i.e. hardwood, conifer, mixed forests). Compared to this, the overall accuracy for forested areas were better for sub-classification of forested areas probably due to less distraction in the classification. To further improve the overall accuracy, it is needed to gain individual imagery rather than mosaic imagery to use more spetral bands and select more suitable conditions such as seasonal timing. It is also necessary to obtain precise and accurate training data for sub-classifying forested areas. This new approach can be considered as a basis of developing an excellent analysis manner for understanding and managing forest landscape.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• 대한치과교정학회지
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• 제47권5호
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• pp.298-305
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• 2017

Objective: This paper describes changes in the characteristics of patients seeking orthodontic treatment over the past decade and the treatment they received, to identify any seasonal variations or trends. Methods: This single-center retrospective cohort study included all patients who presented to Seoul National University Dental Hospital for orthodontic diagnosis and treatment between January 1, 2005 and December 31, 2015. The study analyzed a set of heterogeneous variables grouped into the following categories: demographic (age, gender, and address), clinical (Angle Classification, anomaly, mode of orthodontic treatment, removable appliances for Phase 1 treatment, fixed appliances for Phase 2 treatment, orthognathic surgery, extraction, mini-plate, mini-implant, and patient transfer) and time-related variables (date of first visit and orthodontic treatment time). Time series analysis was applied to each variable. Results: The sample included 14,510 patients with a median age of 19.5 years. The number of patients and their ages demonstrated a clear seasonal variation, which peaked in the summer and winter. Increasing trends were observed for the proportion of male patients, use of non-extraction treatment modality, use of ceramic brackets, patients from provinces outside the Seoul region at large, patients transferred from private practitioners, and patients who underwent orthognathic surgery performed by university surgeons. Decreasing trends included the use of metal brackets and orthodontic treatment time. Conclusions: Time series analysis revealed a seasonal variation in some characteristics, and several variables showed changing trends over the past decade.

• 대한교통학회지
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• 제25권1호
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• pp.121-132
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• 2007

일반국도의 상시조사 자료는 교통량 조사 장비를 통해 수집되며, 수집된 자료가 누락되거나 불량일 경우 통계자료의 객관성을 유지하기 위해서 보정을 해야 한다. 교통량 결측 자료의 보정을 통계적인 방법으로 접근하여 신뢰성을 높이고자 본 연구에서는 보정 대상 시간과 동일시간의 자료를 적용할 수 있는 자기회귀분석과 보정 대상 지점과 동일 지점의 자료를 적용할 수 있는 계절 시계열 분석을 이용하여 보정하는 방안을 제시하였다. 계절 시계열 분석을 적용하여 결측 자료를 보정한 결과, 결측 기간이 길어질수록 오차가 커지는 것으로 분석되었다. 이것은 단기예측의 경우 실제자료를 이용하여 예측 값을 제시하지만, 장기예측의 경우 예측된 자료를 이용하여 예측값을 제시하기 때문에 신뢰성이 떨어지기 때문이라 판단된다. 자기회귀분석을 적용하여 결측 자료를 보정한 결과, 시계열분석에 비해서 오차가 적은 것으로 분석되었다. 이것은 교통량자료는 과거 패턴보다 현재 시점의 영향을 더 많이 받는 것이기 때문이라 판단된다 하지만 자기회귀분석은 인근에 패턴이 유사한 지점이 있어야 가능하며, 인근에 유사한 지점이 있더라도 그 지점의 자료가 불량일 경우 보정이 불가능하다는 단점이 있다. 이러한 경우에는 과거자료를 이용해서 보정할 수밖에 없으며, 단기 결측의 경우에는 시계열분석을 이용할 수 있다.

• 보건행정학회지
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• 제23권3호
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• pp.260-265
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• 2013

Background: The aim of this study was to examine the seasonal variation of death from intentional self-harm by hanging, strangulation and suffocation (HSS: Korean Standard Classification of Diseases-6 code: X70) using the 2011 death registry data. Methods: The analysis was based on data of 8,359 HSS deaths from 2011 national vital statistics in Korea. Daily, weekly, and monthly death pattern on HSS were used to examine the relationship seasonal variation and HSS deaths. Results: A total of 8,359 HSS deaths occurred in 2011, with a mean age of 50.6 years. The HSS death rate (per 100,000) was 25.5 in male and 10.8 in female. In one day 17.6 males and 8.0 females occurred HSS death on average. The number of HSS death per day was the highest on 8th June (45 deaths), and lowest on 1st February (7 deaths) during the period. The variations of daily HSS death showed wide fluctuation from a peak of 34 to 45 deaths (29th May to 9th June) to a trough of 17-26 deaths (10th-13th September: the Korean thank-giving consecutive holidays), 13-20 deaths (2nd-5th February: the new year's day by the lunar calendar) and 8-9 deaths (24th-25th December: Christmas holidays). There were no significant difference between gender and seasonal variation (month, season, and week). Conclusion: The mean number of HSS death per day was highest in June (30.6 deaths), and months with the lowest number of deaths was January and December (range, 19.4 to 19.6 deaths). HSS death were more prevalent during summer and spring and were less likely to occur during winter. On Saturdays (21.0 deaths), the number of HSS death per day was the lowest, and Monday (27.9 deaths) was the highest. HSS death was less likely to occur on holidays (21.4 deaths). There was significant seasonal variation in HSS death by weekly and monthly (p<0.01).

• 한국축산식품학회지
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• 제39권2호
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• pp.332-344
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• 2019

The objective of this study was to investigate slaughtering performance, carcass grade, and quantitative traits of cuts according to seasonal influence by each month in pigs slaughtered in livestock processing complex (LPC) slaughterhouse in Korea, 2017. A total of 267,990 LYD ($Landrace{\times}Yorkshire{\times}Duroc$) pig data were used in this study. Results of slaughter heads, sex distribution, carcass weight, backfat thickness, grading class, total weight, and fat and lean meat percentages of each cut predicted by AutoFom III were obtained each month. The number of slaughtered pigs was the highest in early and late fall but the lowest in midsummer. Only in midsummer that the number of females was higher than that of castrates. During 2017, carcass weight was the lowest in late summer. Backfat thickness was in the range of 21-22 mm. In mid and late spring, pigs showed high 1+ grade ratio (37.05% and 36.15%, respectively). For traits of 11 cuts predicted by AutoFom III, porkbelly showed lower total weight, lean weight, and fat weight in midsummer to early fall but higher lean meat percentage compared to other seasons. Weights of deboned neck, loin, and lean meat were the highest in midfall compared to other seasons (p<0.05). In conclusion, characteristics of slaughtering, grading, and economic traits of pigs seemed to be highly seasonal. They were influenced by seasons. Results of this study could be used as basic data to develop seasonal specified management ways to improve pork production.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.587-605
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• 2006

Recent results in applied statistics have shown that the presence of periodicities in time series may influence the estimation and testing of the fractional differencing parameter. In this article, we provide further evidence on the issue by using several procedures of fractional integration. The results show that in the presence of periodicities, the order of integration can be erroneously detected. An empirical application in the context of seasonal data is also carried out at the end of the article.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 추계 학술논문 발표대회
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• pp.214-215
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• 2014

Recently, Russia decided to export an annual 7.5 million tons of natural gas to Korea over 30 years from 2015, as also deal with China, planed to build a pipeline connecting Siberia to Shandong Peninsula about 4000km. Risk management is required depending on the project in extreme cold weather, because it is concerned about the behavior of the seasonal changes in soil temperature and the strain of pipe according to the long-distance pipeline construction. The plan of data management shall be prepared in parallel for a sophisticated risk management, because a data is massive scale and it is generated/accumulated in real time. Therefore, this research is aimed to classify a data items in engineering stage of pipeline by previous studies for managing a generated data depending on the detail works in extreme cold weather. We expect to be provided the foundation of an efficient classification system of a generated data from the pipeline project life cycle.