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Highly efficient process capability indices under contaminated data
Donggeun Lee,Ryeji Jung 대한산업공학회 2017 대한산업공학회 추계학술대회논문집 Vol.2017 No.11
In order to improve manufacturing process, process capability indices have been popularly used. Process capability represents the performance of a process when condition is normal and in-control. This paper propose a new process capability indices based on the Hodge Lehmann and Shamos estimators. The new method performs well under the normal condition and outperforms the existing methods under the contamination.
A Simple Carbamidomethylation-Based Isotope Labeling Method for Quantitative Shotgun Proteomics
( Donggeun Oh ),( Sun Young Lee ),( Meehyang Kwon ),( Sook Kyung Kim ),( Myeong Hee Moon ),( Dukjin Kang ) 한국질량분석학회 2014 Mass spectrometry letters Vol.5 No.3
In this study, we present a new isotope-coded carbamidomethylation (iCCM)-based quantitative proteomics, as a complementary strategy for conventional isotope labeling strategies, with providing the simplicity, ease of use, and robustness. In iCCM-based quantification, two proteome samples can be separately isotope-labeled by means of covalently reaction of all cysteinyl residues in proteins with iodoacetamide (IAA) and its isotope (IAA-13C2, D2), denoted as CM and iCCM, respectively, leading to a mass shift of all cysteinyl residues to be + 4 Da. To evaluate iCCM-based isotope labeling in proteomic quantification, 6 protein standards (i.e., bovine serum albumin, serotransferrin, lysozyme, beta-lactoglobulin, beta-galactosidase, and alpha-lactalbumin) isotopically labeled with IAA and its isotope, mixed equally, and followed by proteolytic digestion. The resulting CM-/iCCM-labeled peptide mixtures were analyzed using a nLC-ESI-FT orbitrap-MS/MS. From our experimental results, we found that the efficiency of iCCM-based quantification is more superior to that of mTRAQ, as a conventional nonisobaric labeling method, in which both of a number of identified peptides from 6 protein standards and the less quantitative variations in the relative abundance ratios of heavy-/light-labeled corresponding peptide pairs. Finally, we applied the developed iCCM-based quantitative method to lung cancer serum proteome in order to evaluate the potential in biomarker discovery study
Donggeun Kim,Sangwoo Park,Donggoo Kang,Joonki Paik 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.1
A single shot multibox detector (SSD) is used as a baseline for many object detection networks, since it can provide sufficiently high accuracy in real time. However, it cannot deal with objects of various sizes, because features used in an SSD are not robust to multi-scale objects. To solve this problem, we present an improved feature pyramid for using multi-scale context information. The proposed feature pyramid fuses only adjacent features of the conventional SSD to achieve high accuracy without decreasing the processing speed. Our detector, with a 320×320 input, achieved 79.1% mean average precision (mAP) at 63 frames per second on a Pascal Visual Object Classes Challenge 2007 test set using a single Nvidia 1080 Ti graphics processing unit. This result shows better performance than existing SSDs.
Human-Leg Detection in 3D Feature Space for a Person-Following Mobile Robot Using 2D LiDARs
Donggeun Cha,Woojin Chung 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.7
People detection is an essential technique for person-following mobile robots in applications of human-friendly services and collaborative tasks. 2D light detection and ranging (LiDAR) sensors are useful for these applications, especially for applications that detect and follow people while maintaining a suitable distance, at a close range, using accurate range measurements. In this study, we propose a method of human-leg detection in 3D feature space for a person-following mobile robot equipped with a 2D LiDAR sensor. We also propose an improved LiDAR scan segmentation technique to extract segments of human leg candidates. The newly proposed method generates a feature vector with the attributes of leg shapes and learns a classification boundary in 3D feature space. Experimental results indicate that the proposed method successfully describes the target dataset and provides accurate leg detection. This study demonstrates that human legs can be detected with improved accuracy by learning the classification boundary in 3D feature space.