• Title/Summary/Keyword: PCA(Principal Component Analysis

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Flood Risk Assessment Based on Bias-Corrected RCP Scenarios with Quantile Mapping at a Si-Gun Level (분위사상법을 적용한 RCP 시나리오 기반 시군별 홍수 위험도 평가)

  • Park, Jihoon;Kang, Moon Seong;Song, Inhong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.55 no.4
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    • pp.73-82
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    • 2013
  • The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

Hough Transform Using Straight Line Information of Edge Pixels (에지 화소들의 직선 정보를 이용한 허프변환)

  • Kim, Jin-tae;Oh, Jeong-su
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.10a
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    • pp.674-677
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    • 2017
  • The Hough transform is the most representative algorithm for a straight line detection based on edge pixels. It shows excellent performance in a simple linear image but requires a considerable amount of computation in a noisy or complex image and has a problem of detecting a pseudo straight line easily. In this paper, we propose a straight line detection algorithm to solve the problem of the conventional Hough transform. The proposed algorithm detects the straight line information of edge pixels by using principal component analysis (PCA) before performing Hough transform and performs the Hough transform of the limited slope area in the valid edge pixels based on the detected straight line information of edge pixels. Simulation results show that the proposed algorithm reduces the amount of computation as well as eliminates pseudo straight lines.

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Effects of Adding Green Grape Juice on Quality Characteristics of Konjak Jelly (청포도 즙의 첨가가 곤약젤리의 품질특성에 미치는 영향)

  • Jeon, Jae-Eun;Lee, In-Seon
    • Journal of the Korean Society of Food Culture
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    • v.34 no.5
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    • pp.629-636
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    • 2019
  • This study examined the quality characteristics of jelly prepared with green grape juice (GJ). The pH, $^{\circ}Brix$ value, color, texture, and sensory evaluation of the jelly were measured. The pH of the jelly significantly decreased with increasing amount of GJ over the range of 3.25-5.27. The $^{\circ}Brix$ value of the jelly showed a significantly higher result as the amount of GJ increased (p<0.001). Lightness (L) and redness (a) decreased with increasing amount of GJ, and yellowness (b) increased. In the texture measurement, the GJ-100 sample group with a high substitute rate of GJ showed high hardness, gumminess, and chewiness (p<0.001). The results of principal component analysis (PCA) showed that the sample groups with high GJ content were classified as having relatively strong yellowness, sweet aroma, metallic aroma, grassy aroma, sweetness, sourness, green grape skin taste, and astringency. In the acceptance test, the GJ-50 sample group was evaluated to be high in flavor (p<0.001) and overall acceptance (p<0.01). However, sample groups consisting of 50% or more GJ were evaluated to be significantly strong in terms of astringency. Therefore, further study needs to be conducted about improving astringency in the future.

Evaluation of Larynx Cancer via Chemometrics Assisted Raman Spectroscopy

  • Senol, Onur;Albayrak, Mevlut
    • Current Optics and Photonics
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    • v.3 no.2
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    • pp.150-153
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    • 2019
  • Larynx cancer is a potentially terminal and severe type of neck and head cancer in which malignant cells start to grow and spread upwards in the larynx, or voice box. Smoking tobacco, drinking hot beverages and drinking alcohol are the main risk factors for these tumors. In this study, we aimed to develop a precise, accurate and rapid chemometrics assisted Raman spectroscopy method for diagnosis of larynx cancer in deparaffinized tissue samples. In the proposed method, samples were deparaffinized and 20 microns of each tissue were located on a coverslip. Both healthy (n = 13) and cancerous tissues (n = 13) were exposed to a Raman laser (785 nm) and excitations were recorded between wavenumbers of $50{\sim}1500cm^{-1}$. An Orthogonal Partial Least Square algorithm was applied to evaluate the Raman spectrum obtained. Sensitivity and specificity of the proposed method is high enough with the aid of Principal Component Analysis (PCA) to test the whole model. Healthy and cancerous tissues were accurately and precisely clustered. A rapid, easy and precise diagnosis algorithm was developed for larynx cancer. By this method, some useful data about differences in biomolecules of each group (phospholipids, amides, tyrosine, phenylalanine collagen etc.) was also obtained from the spectra. It is claimed that the optimized method has a great potential for clustering and separating tumor tissues from healthy ones. This novel, rapid, precise and objective diagnosis method may be an alternative for the conventional methods in literature for diagnosis of larynx cancer.

Review of Wind Energy Publications in Korea Citation Index using Latent Dirichlet Allocation (잠재디리클레할당을 이용한 한국학술지인용색인의 풍력에너지 문헌검토)

  • Kim, Hyun-Goo;Lee, Jehyun;Oh, Myeongchan
    • New & Renewable Energy
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    • v.16 no.4
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    • pp.33-40
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    • 2020
  • The research topics of more than 1,900 wind energy papers registered in the Korean Journal Citation Index (KCI) were modeled into 25 topics using latent directory allocation (LDA), and their consistency was cross-validated through principal component analysis (PCA) of the document word matrix. Key research topics in the wind energy field were identified as "offshore, wind farm," "blade, design," "generator, voltage, control," 'dynamic, load, noise," and "performance test." As a new method to determine the similarity between research topics in journals, a systematic evaluation method was proposed to analyze the correlation between topics by constructing a journal-topic matrix (JTM) and clustering them based on topic similarity between journals. By evaluating 24 journals that published more than 20 wind energy papers, it was confirmed that they were classified into meaningful clusters of mechanical engineering, electrical engineering, marine engineering, and renewable energy. It is expected that the proposed systematic method can be applied to the evaluation of the specificity of subsequent journals.

Diagnosis of Alzheimer's Disease using Combined Feature Selection Method

  • Faisal, Fazal Ur Rehman;Khatri, Uttam;Kwon, Goo-Rak
    • Journal of Korea Multimedia Society
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    • v.24 no.5
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    • pp.667-675
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    • 2021
  • The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

Comparison of ecophysiological and leaf anatomical traits of native and invasive plant species

  • Rindyastuti, Ridesti;Hapsari, Lia;Byun, Chaeho
    • Journal of Ecology and Environment
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    • v.45 no.1
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    • pp.24-39
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    • 2021
  • Background: To address the lack of evidence supporting invasion by three invasive plant species (Imperata cylindrica, Lantana camara, and Chromolaena odorata) in tropical ecosystems, we compared the ecophysiological and leaf anatomical traits of these three invasive alien species with those of species native to Sempu Island, Indonesia. Data on four plant traits were obtained from the TRY Plant Trait Database, and leaf anatomical traits were measured using transverse leaf sections. Results: Two ecophysiological traits including specific leaf area (SLA) and seed dry weight showed significant association with plant invasion in the Sempu Island Nature Reserve. Invasive species showed higher SLA and lower seed dry weight than non-invasive species. Moreover, invasive species showed superior leaf anatomical traits including sclerenchymatous tissue thickness, vascular bundle area, chlorophyll content, and bundle sheath area. Principal component analysis (PCA) showed that leaf anatomical traits strongly influenced with cumulative variances (100% in grass and 88.92% in shrubs), where I. cylindrica and C. odorata outperformed non-invasive species in these traits. Conclusions: These data suggest that the traits studied are important for plant invasiveness since ecophysiological traits influence of light capture, plant growth, and reproduction while leaf anatomical traits affect herbivory, photosynthetic assimilate transport, and photosynthetic activity.

A Study on the Impact of Firm Size on the Threshold Point from Nonlinear Relationship between CSR and Firm Value (기업의 규모별 특성이 사회적 책임과 기업가치 간의 비선형 관계를 유발하는 임계점에 미치는 영향에 대한 연구)

  • Kim, Jong-Hee
    • Asia-Pacific Journal of Business
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    • v.11 no.2
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    • pp.207-233
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    • 2020
  • Purpose - The purpose of this paper analyzes the relationship between the Corporate Social Responsibility(CSR) and Corporate Value to estimate whether the characteristics of Firm can change this relationship. Design/methodology/approach - This paper utilizes the total 776 firms' data over the period 2014-2018, and develops a new ESG index which was estimated by PCA. Findings - First, the estimated ESG index implies that Large company has the highest value of CSR, while Medium sized and Small company have the relatively low one. And comparing to the case of 2014, the trend of ESG index in Large company does not decrease in 2018. Second, there is a clear and significant non linear relationship between CSR and corporate value, it implies that the U-shaped exists in the Korean Firms. Such a tendency is mush stronger in the Large company. Third, the new ESG index indicates that it takes more time to increase Firm value in the Medium sized and Small company while there is a high possibility of increasing value in Large company from the little gab between the threshold points and mean value of ESG. Research implications or Originality - The non linear tendency between the Corporate Social Responsibility and Corporate Value is strongly affected by Firm size and the relative high quintile of ESG, but it is less affected by Firm history.

Development of an Efficient 3D Object Recognition Algorithm for Robotic Grasping in Cluttered Environments (혼재된 환경에서의 효율적 로봇 파지를 위한 3차원 물체 인식 알고리즘 개발)

  • Song, Dongwoon;Yi, Jae-Bong;Yi, Seung-Joon
    • The Journal of Korea Robotics Society
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    • v.17 no.3
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    • pp.255-263
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    • 2022
  • 3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

A Study on the Sensory Characteristics and Consumer Preferences for the Development of Food Menus Using Agricultural Products in Chungju (충주 지역농산물을 활용한 메뉴 개발을 위한 관능적 특성 및 소비자 기호도 조사)

  • Jeong-Eun Yang;Hojin Lee
    • The Korean Journal of Food And Nutrition
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    • v.36 no.4
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    • pp.274-285
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    • 2023
  • This study was conducted to select representative agricultural products (4 types of fruits and 4 types of wild vegetables) in Chungju, define their sensual characteristics, derive suitable flavour-pairing and recipes for each ingredient, and use them as a cornerstone in the development of menus. For the experiment, 10 experts were selected to choose 8 representative agricultural products in Chungju, and 18 menus were selected through a flavour-pairing survey. A consumer panel (a total of 413 people, 105 in their 20s, 103 in their 30s, 103 in their 40s, and 102 in their 50s) for evaluating the characteristics of consumer preferences was selected. After the flavour-pairing survey 'sweet taste', 'light flavour', 'soft flavour', 'savoury flavour', 'familiar flavour', 'harmonious flavour', 'softness', and 'harmoniousness with food ingredients' were determined as drivers of liking, on the other hand, 'disturbance with food ingredients' and 'soybean fishy smell' were determined as drivers of disliking. The degree of consumer preference and overall acceptance were found to be related to the consumers' familiarity, suggesting that if a menu should be developed using unfamiliar local agricultural products, it should be configured with familiar recipes and seasoning methods.