• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• 한국산업정보학회논문지
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• 제27권5호
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• pp.73-82
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• 2022

고혈압과 같은 만성질환은 발병의 원인은 다양한 요인들이 복합적으로 작용하기 때문에 생애주기에 따라 차별화된 관리가 필요하다. 본 연구는 머신러닝을 이용해 고혈압 발병에 영향을 미치는 요인들의 생애주기별로 차이를 분석한다. 이를 위해, 질병관리청의 국민건강영양조사 데이터에 대한 전처리 및 변수 선택 과정을 거쳐 총 35개의 변수를 활용했다. 분석결과, 트리기반 머신러닝 모델 중 XGBoost가 중년과 노년 모두 예측 성능이 높은 모델로 나타났다. 변수중요도를 통해 도출된 생애주기별 고혈압 위험요인을 살펴보면 중년의 경우 개인특성 요인, 유전적 요인, 영양섭취 요인이 고혈압 위험요인으로 나타났고, 노년의 경우 영양섭취 요인, 식생활 요인, 생활습관 요인이 고혈압 위험요인으로 도출되었다. 본 연구 결과는 생애주기별 고혈압 관리에 유용한 기초자료로 사용될 수 있을 것으로 기대된다.

• 대한의용생체공학회:의공학회지
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• 제44권1호
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• pp.11-18
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• 2023

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 해양환경안전학회지
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• 제28권6호
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• pp.992-1001
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• 2022

해상교통분석은 복잡해지는 해양환경에 따라 발생하는 문제해결을 위해 다방면으로 시행되고 있다. 하지만 4차 산업혁명으로부터 도래된 자율운항선박 개발 등의 해사분야 동향은 해상교통분석에도 변화가 필요함을 암시한다. 이에 해상교통분석의 개선점을 식별하고자 관련 연구를 분석하였으며, AIS데이터의 활용도가 높은 반면에 해도정보의 활용은 그 중요도에 비해 부족한 것으로 조사되었다. 이에 본 연구는 자율운항선박의 상용화에 대비한 해상교통분석의 개선점으로서 수치해도 데이터와 선박운항데이터인 AIS데이터를 복합적으로 활용하는 방법을 제시하였다. 연구결과로써 해상교통분석에 수치해도데이터를 활용하였을 때 추출 가능한 해상교통특성을 제시하였으며 이는 향후 자율운항선박의 도입을 위한 해상교통분석에 활용가능할 것으로 기대된다.

• 미래기술융합논문지
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• 제2권3호
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• pp.17-24
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• 2023

하이브리드 자동차를 연구로 목적으로 논문을 설계합니다. 히이브리드 자동차는 내연 기관과 배터리 엔진을 동시에 장착하여 기존의 일반 차량에 비해 연비와 유해가스 배출량을 획기적으로 줄인 차세대 자동차로 전기모터는 차량 내부에 장착된 고전압 배터리로부터 전원을 공급받고, 배터리는 자동차가 움직일 때 다시 충전되는 시스템을 가진 차를 연구로 기존 자동차의 에너지 손실은 대부분 교통 혼잡에 따른 공회전을 보완해 차량의 속도나 주행 상태 등에 따라 시간과 차량 정지시 발생하는데 하이브리드 시스템은 이 같은 가솔린 또는 디젤 엔진의 단점엔진과 모터 힘을 적절하게 제어함으로써 효율성을 극대화시킨 차라고 할 수 있다. 하이브리드 차는 연비 개선, 환경 친화성, 경제성, 노이즈 감소 등 다양한 장점을 가지고 있어서 점차 많은 사람들에게 선택되고 있는 대안적인 자동차 옵션입니다. 해당 연구를 통해 환경오염을 방지하고 미래 자동차 연구를 위해 노력을 기울일수 있을 것입니다.

• Journal of Biosystems Engineering
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• 제22권2호
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• pp.237-246
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• 1997

굴 까는 작업을 자동화하기 위한 방법으로 굴의 껍질 안쪽에 붙어 있는 근육질을 제거하고 굴 껍질의 힌지(Hinge)를 절단하는 작업을 필요로 한다. 본 논문에서는 굴을 까는 자동화 기계를 개발하기 위한 연구의 일환으로써 컴퓨터 시각 시스템을 이용하여 굴의 힌지 위치를 판단하는 영상처리 알고리즘을 개발하였다. 본 실험에 사용한 굴들은 컴퓨터 비젼 시스템이 굴의 바깥쪽 힌지표면을 감지할 수 있도록 굴을 물로 씻은 후굴 껍질의 힌지(Hinge)부분을 약간 절단하였다. 칼라 비디오 카메라(color video camera)를 이용하여 굴의 절단된 힌지표면의 영상을 잡은 후 개발한 영상처리 알고리즘을 이용하여 굴의 힌지(hinge)위치를 감지하였다. 영상내의 굴의 힌지(Hinge)와 그 밖의 다른 물체를 구별하기 위하여 4개의 변수 (원형도, 사각형도, 장단축비, 유크리드(Euclidian)거리 )를 이 용하였다. 또한 영상(image)내 의 굴의 힌지(Hinge) 위치를 쉽고 효과적으로 파악하기 위하여 몇 가지 영상처리 즉, 수축-확장, 문턱갑 처리 등의 방법들을 이용하였다.

• 농촌계획
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• 제28권1호
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• 한국의상디자인학회지
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• 제25권3호
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• pp.97-111
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• 2023

Dynamic orphism images expressed with abstraction and construction in color are often found in modern fashion. Orphism stressing color is one of the many art trends that influence fashion. This work aims to examin its expressive effects and characteristics that appear in fashion and provide basic materials for fashion design study. This study proceeds with the following steps. First, the study takes on a theoretical examination of the orphism trend using a literature review. Second, based on its results, modern fashion's expressive features influenced by orphism are analyzed. Third, the study draws expression effects in modern fashion from these characteristics. The study coverage is confined to domestic and foreign collections released in the 21st century. The study contents come from an Internet-base domestic and international database and published material, including dissertations and books. Orphism expression effects are found in the following ways in modern fashion. First, it lies in the abstraction effect of color. This feature arrays flat geometric figure on the clothing surface and applies a strong sense of color inside it, as if color has an abstractive structured design. Second, it gives a dynamic sense visually to the clothes all at once by stressing the relation among and between the colors with powerful contrast. Third, it has a collage effect of color. This effect develops into an organic combination composed of geometric plane figures with intense complementary colors in a single piece of clothing. Expression types that presents the orphism effect in fashion are divided into clothing showing traditional orphism characteristics faithfully and clothing expressing conventional orphism characteristics mixed with modern trends. Further developed orphism fashion can be classified by the playful type, with graffiti added onto an orphism color structure, and the destructive type, where traditional orphism color orders are taken apart and reformed into a new color order.

• 한국음향학회지
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• 제28권6호
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• pp.566-571
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• 2009

본 논문에서는 eigenvoice 방식에 기반하여 다양한 잡음 환경에 강인한 고속 화자 적응 방법을 제안하였다. 제안된 방법은 잡음 제거 기술과 환경 군집화 방법을 기반으로 한다. 그러나, 잡음 제거 기술을 통해 잡음을 제거한 후에도 여전히 잔여 잡음이 존재하므로 비음성 구간의 켑스트럼 평균을 사용하여 잡음 환경별로 화자 적응 데이터를 분류한 후 각각의 환경별로 환경 모델을 구성한다. 이러한 환경 군집화를 적응데이터에 대해 구성한 후 테스트 음성이 입력되면 군집화된 모델 중에서 인식 데이터와 가장 유사한 복수의 환경별 군집화된 화자 적응 모델을 구한 후 이들의 가중함을 통해 화자 적응을 수행하는 방법이다. 제안된 방법은 적응 및 평가를 통해 화자 독립 모델을 사용한 경우에 비해 $40{\sim}59%$ 인식 오류 감소율을 얻었다.