본 연구의 목적은 2023년 4월 충청남도 홍성군 대형산불피해지를 대상으로 산불로 인한 온실가스 배출량을 산정하여 국가 온실가스 인벤토리 고도화에 기여하고자 한다. 산불로 인한 온실가스 배출량은 2006년 IPCC 가이드라인에 따라 산정하였으며, 산정 인자인 연소면적은 Sentinel-2A 위성영상 기반의 differenced Normalized Burn Ratio (dNBR)을 활용하여 제작한 산불피해등급도를 이용하였고 지표층 및 수관층의 연료량 및 연소효율은 현장자료를 바탕으로 추정하였다. dNBR을 활용하여 제작한 산불피해등급도를 기반으로 산정한 온실가스 배출량은 약 19,336.9톤으로, 국립산림과학원 자료를 이용한 결과보다 약 4.0% 증가한 것으로 나타났다. 본 연구는 현장자료를 반영하여 산불로 인한 온실가스 배출량을 보다 정밀하게 산정한 데 의의가 있다. 향후에는 국내 생태계 특성을 반영한 각 요소별 고유 지표의 도입이 요구된다.

PURPOSES : For autonomous vehicles, abnormal situations, such as sudden changes in driving speed and sudden stops, may occur when they leave the operational design domain. This may adversely affect the overall traffic flow by affecting not only autonomous vehicles but also the driving environment of manual vehicles. Therefore, to minimize the traffic problems and adverse effects that may occur in mixed traffic situations involving manual and autonomous vehicles, an autonomous vehicle driving support system based on traffic operation optimization is required. The main purpose of this study was to build a big-data-classification system by specifying data classification to support the self-driving of Lv.4 autonomous vehicles and matching it with spatio-temporal data. METHODS : The research methodology is explained through a review of related literature, and a traffic management index and big-dataclassification system were built. After collecting and mapping the ITS history traffic information data of an actual Living Lab city, the data were classified using the traffic management indexing method. An AI-based model was used to automatically classify traffic management indices for real-time driving support of Lv.4 autonomous vehicles. RESULTS : By evaluating the AI-based model performance using the test data from the Living Lab city, it was confirmed that the data indexing accuracy was more than 98% for the KNN, Random Forest, LightGBM, and CatBoost algorithms, but not for Logistics Regression. The data were severely unbalanced, and it was necessary to classify very low probability nonconformities; therefore, precision is also important. All four algorithms showed similarly good performances in terms of accuracy. CONCLUSIONS : This paper presents a method for efficient data classification by developing a traffic management index to easily fuse and analyze traffic data collected from various institutions and big data collected from autonomous vehicles. Additionally, EdgeRSU is presented to support the driving of Lv.4 autonomous vehicles in mixed autonomous and manual vehicles traffic situations. Finally, a database was established by classifying data automatically indexed through AI-based models to quickly collect and use data in real-time in large quantities.

기존 항만 건설 시 화물차 전용 주차장이 고려되지 않았으며, 해양수산부의 ‘제2차 신항만건설기본계획(2019~2040)’에 따라 총 11 개의 새로운 항만이 건설될 예정이다. 따라서 화물차 전용 주차장 설계에 대한 연구가 필요한 실정이다. 현재 항만에서는 화물차 전용 주차 공간 부족으로 불법 주차가 발생하고 있으며, 이로 인해 교통사고 위험이 증가하고 있다. 기존 연구에서는 전체 항만을 대상으로 한 분류 방법이 제안되었으나, 신설 항만 설계 시 과소 또는 과대 설계 문제를 초래한다. 따라서 본 연구는 부두별로 4대 요소(안벽 길이, 야적장 면적, 접안 능력, 하역 능력)를 기반으로 분류하며, DWT와 TEU 단위를 고려하여 데이터를 분석하였다. 14개 국가 관리 항만의 총 380 부두 데이터를 조사하고, 이를 통해 그룹핑 작업을 통해 정규화 곡선으로 평균 ± 표준편차를 기준으로 항만 전체 부두 에 대한 분류를 실시하였다. 이를 통해 향후 연구결과를 통해 검증 후 최종 분류방법을 결정하여 새로운 항만분류법을 제안하고, 제안 된 방법론의 분류검증을 실시할 예정이다.

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

본 연구는 산림자원으로 활용 가능한 활엽수림(혼효림 포함)의 상층 교목성 수종을 대상으로 유사한 수종구성을 갖는 산림 유형 분류를 통해 활엽수림의 효율적 산림관리 기반 구축을 위해 실시하였다. 제7차 국가산림자원조사 고정표본점(3,994지점) 중 2,291지점의 임분 현황 자료를 기반으로 클러스터분석, 지표종분석, 다중판별분석 등 다양한 다변량 통계분석 기법을 이용하여 산림 유형을 분류하였다. 활엽수림의 산림 유형은 신갈나무-피나무림, 신갈나무-당단풍나무림, 신갈나무-물푸레나무림, 신갈나무-굴참나무림, 신갈나무-소나무림, 졸참나무-굴참나무림, 소나무-신갈나 무림, 소나무-굴참나무림, 밤나무-아까시나무림, 상수리나무-소나무림 등 10개로 구분되었다. 신갈나무와 기타 활엽수림, 신갈나무가 우점하는 참나무 림, 주요 참나무류 수종과 소나무의 혼효림 형태로 구성된 것으로 파악되었다. 조사된 102종의 수종 중 점유비율이 높은 신갈나무, 굴참나무 및 소나무가 산림유형 분류에 중요한 역할을 한 것으로 나타났다.

Along with the current rapid development of technology, object classification is being researched, developed, and applied to security systems, autonomous driving, and other applications. A common technique is to use vision cameras to collect data of objects in the surrounding environment. Along with many other methods, LiDAR sensors are being used to collect data in space to detect and classify objects. By using the LiDAR sensors, some disadvantages of image sensors with the negative influence on the image quality by weather and light condition will be covered. In this study, a volumetric image descriptor in 3D shape is developed to handle 3D object data in the urban environment obtained from LiDAR sensors, and convert it into image data before using deep learning algorithms in the process of object classification. The study showed the potential possibility of the proposal and its further application.

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

머신러닝 기법의 발달과 함께 기계에서 발생하는 다양한 종류(진동, 온도, 유량 등)의 데이터를 활용하여 기계의 상태를 진단하고 이상 탐지 및 비정상 분류 연구도 활발히 진행되고 있다. 특히 진동 데이터를 활용한 회전 기계의 상태 진단은 전통적인 기계 상태 모니터링 분야로 오랜 기간 동안 연구가 진행되었고, 연구 방법 또한 매우 다양하다. 본 연구에서는 가정용 에어컨에 사용되는 로터리 압축기에 가속도계를 직접 설치하여 진동 데이터를 수집하는 실험을 진행하였다. 데이터 부족 문제를 해결하기 위해 데이터 분할을 수행하였으며, 시간 영역에서의 진동 데이터로부터 통계적, 물리적 특징들을 추출한 후, Chi-square 검증을 통해 고장 분류 모델의 주요 특징을 추출하였다. SVM(Support Vector Machine) 모델은 압축기의 정상 혹은 이상 유무를 분류하기 위해 개발되었으며, 파라미터 최적화를 통해 분류 정확도를 개선하였다.

The purpose of this study is to obtain data on the zones formed by the movement of livestock vehicles and to determine if such areas can be used to establish quarantine activities and quarantine policies for livestock epidemics. For this purpose, this study used mobile data on poultry-related livestock vehicles generated in 2019. InfoMap, a community detection method, was used for regional classification, and the results of the analysis were visualized on a map using GIS. The study results confirmed that the zone of the administrative unit can be classified based on the movement of livestock vehicles. In addition, the zones created by the vehicle movement could be seen to change depending on the purpose and timing of the operation of livestock vehicles. Some areas form relatively stable zones, such as Jeolla-do and Gyeongsang-do, while others change depending on the situation, such as Chungcheong-do, Gyeonggi-do, and Gangwon-do. Further, the zones derived for poultry differed from those derived for cattle and pigs in previous studies.

국내에는 독자적으로 연구가 수행되어 개인적으로 보관 중인 지질 연구 자료가 다량 존재하는데, 이 자료에 대한 접근성이 떨어지기 때문에 다른 연구자들과의 공유가 용이하지 않다. 이런 자료에 대한 메타데이터를 체계적으로 구축하고 총괄적으로 관리하여 이 자료를 필요로 하는 연구자들이 효과적으로 연구를 수행할 수 있는 기회를 제공하는 것이 이 연구의 목적이다. 국내에서 연구된 약 1000여개의 지질 시료(900여개의 암석과 화석 시료, 100여개의 박편 시 료)를 수집하였고, 각 시료의 고화질 사진, 분류, 시료명, 보유기관, 산지, 좌표, 특징 등에 대한 메타데이터를 구축하였다. 암석과 화석 시료 100개에 대해 추가적으로 3D 모델링을 수행하였다. 이 연구를 통해 유실되거나 방치되는 중요한 지질 자료에 대한 연구자들의 접근성이 높아지고 자료의 공유가 가능해진다. 따라서 연구자들은 반복적인 연구 자료 수 집 작업으로 인한 시간과 비용의 낭비를 줄일 수 있고, 효율적인 연구를 수행하여 경쟁력을 갖춘 연구 결과를 획득할 수 있다. 또한 이미 확보된 시료에 대한 무분별한 반복 채집으로 인해 중요한, 그리고 피해에 취약한 자료가 훼손되는 것을 방지할 수 있다. 향후 전국의 대학과 연구기관에서 보관중인 다양한 암석과 박편 시료에 대한 메타데이터를 추가로 구축하면 자료의 식별 및 진전된 연구가 가능하고, 더불어 전문적인 광물학 및 암석학의 기초 지식에 대한 비교와 분석을 기대할 수 있다.