• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.1-14
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• 2017

A land cover map provides basic information to help understand the current state of a region, but its utilization in the ecological research field has deteriorated due to limited temporal and spatial resolutions. The purpose of this study was to investigate the possibility of using a land cover map with data based on high resolution images acquired by UAV. Using the UAV, 10.5 cm orthoimages were obtained from the $2.5km^2$ study area, and land cover maps were obtained from object-based and pixel-based classification for comparison and analysis. From accuracy verification, classification accuracy was shown to be high, with a Kappa of 0.77 for the pixel-based classification and a Kappa of 0.82 for the object-based classification. The overall area ratios were similar, and good classification results were found in grasslands and wetlands. The optimal image segmentation weights for object-based classification were Scale=150, Shape=0.5, Compactness=0.5, and Color=1. Scale was the most influential factor in the weight selection process. Compared with the pixel-based classification, the object-based classification provides results that are easy to read because there is a clear boundary between objects. Compared with the land cover map from the Ministry of Environment (subdivision), it was effective for natural areas (forests, grasslands, wetlands, etc.) but not developed areas (roads, buildings, etc.). The application of an object-based classification method for land cover using UAV images can contribute to the field of ecological research with its advantages of rapidly updated data, good accuracy, and economical efficiency.

• Journal of Drive and Control
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• v.13 no.3
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• pp.53-66
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• 2016

New non-hybrid approaches to improve energy efficiency of construction machineries are reviewed in this paper. Hydraulic systems are classified into four classes according to Backe's classification and commercially promising new technologies are carefully chosen in each class. IMV, 3-Line CPR, Closed Circuit Displacement Control of Differential Cylinder, and Throttle-less Secondary Control are chosen as representative non-hybrid new technologies. Key principle of each technology is explained and representative references which run through each technology are selected. Advantages and weaknesses of each technology are discussed and compared from the view point of construction machinery manufacturers.

• Food Science of Animal Resources
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• v.42 no.1
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• pp.1-17
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• 2022

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.

• The Journal of Internal Korean Medicine
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• v.31 no.1
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• pp.1-10
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• 2010

Objectives : It is necessary that the international classification of diseases (ICD) be examined in order to comprise the third revision of the Korean Classification of Disease on Oriental Medicine (KCD-OM) and disease classification in the oriental internal medicine field. It is essential that the selection, classification and definition of disease and pattern names of oriental concepts in internal medicine be clear. Since 2008, the fifth revision of the Korean Classification of Disease (KCD-5) has been used in Korea. It was required to use the reference classification from the Oriental medicine area based on the ICD-10. Methods : In this review, the necessity for, meaning of and content of the third revision are briefly described. The ICD system was reviewed and KCD-OM was reconstructed. How diagnosis in the oriental internal medicine area had changed is discussed. Review and Results : In 1973, the disease classification of oriental medicine was established the basis on the contents of Dongeuibogam. It was irrespective of the ICD. As to the classification system in the Oriental internal medicine field, systemic disease was comprised of wind, cold, warm, wet, dryness, heat, spirit, ki, blood, phlegm and retained fluid, consumptive disease, etc. Diseases of internal medicine comprised a system according to the five viscera and the six internal organs and followed the classification system of Dongeuibogam. The first and second revisions were of the classification system based on the curriculum in 1979 and 1995. In 1979, in the first revision, geriatric disease and idiopathic types of disease were deleted, and skin disease was included among surgery diseases. This classification was expanded to 792 small classification items and 1,535 detailed classification items to the dozen disease classes. In 1995, in the second revision, it was adjusted to 644 small classes and 1,784 detailed classification items in the dozen disease classes. KCD-OM3 did KCD from this basis. It added and comprised the oriental medical doctor's concept names of diseases considering the special conditions in Korea. KCD-OM3 examined the KCD-OMsecond revised edition (1994). It improved the duplex classification, improper classifications, etc. It is difficult for us to separate the disease names and pattern names in oriental medicine. We added to the U code and made one classification system. By considering the special conditions in Korea, 169 codes (83 disease name codes, 86 pattern name codes) became the pre-existence classification and links among 306 U codes of KCD-OM3. 137 codes were newly added in the third revision. U code added 3 domains. These are composed of the disease name (U20-U33, 97 codes), the disease pattern name (U50-U79, 191 codes) and the constitution pattern name of each disease (U95-U98, 18 codes). Conclusion : The introduction of KCD-OM3 conforms to the diagnostic system by which oriental medical doctors examine classes used with the basic structure of the reference classification of WHO and raises the clinical study and academic activity of the Korean oriental medicine and makes the production of all kinds of nation statistical indices possible. The introduction of KCD-OM3 promotes the diagnostic system by which doctors of Oriental medicine examine classes using the association with KCD-5. It will raise the smoothness and efficiency of oriental medical treatment payments in the health insurance, automobile insurance, industrial accident compensation insurance, etc. In addition, internationally, the eleventh revision work of the ICD has been initiated. It needs to consider incorporating into the International Classification of Diseases some of every country's traditional medicine.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.5
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• pp.407-415
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• 2016

Recent wireless communication trends have emphasized the importance of energy-efficient transmission. In this paper, link adaptation with machine learning mechanism for maximum energy efficiency in multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) wireless system is considered. For reflecting frequency-selective MIMO-OFDM channels, two-dimensional capacity(2D-CAP) feature space is proposed. In addition, machine-learning-based bit and power adaptation(ML-BPA) algorithm that performs classification-based link adaptation is presented. Simulation results show that 2D-CAP feature space can represent channel conditions accurately and bring noticeable improvement in link adaptation performance. Compared with other feature spaces, including ordered postprocessing signal-to-noise ratio(ordSNR) feature space, 2D-CAP has distinguished advantages in either efficiency performance or computational complexity.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.239-251
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• 2016

Hyperspectral image is widely used for land cover classification because it has a number of narrow bands and allow each pixel to include much more information in comparison with previous multi-spectral image. However, Higher spectral resolution of hyperspectral image results in an increase in data volumes and a decrease in noise efficiency. SAM(Spectral Angle Mapping), a method based on vector inner product to compare spectrum distribution, is a highly valuable and popular way to analyze continuous spectrum of hyperspectral image. SAM is shown to be less accurate when it is used to analyze hyperspectral image for land cover classification using spectral library. this inaccuracy is due to the effects of atmosphere. We suggest a decision tree based method to compensate the defect and show that the method improved accuracy of land cover classification.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.48-55
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• 2009

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.