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학술 7 특별구두세션 : C5. 특별세션 품질선진기법 ; MCS(Monte Carlo Simulation)을 활용한 공정능력 예측
민경현 ( Kyung Hyun Min ),이레테크 ( Eretec Inc ) 한국품질경영학회 2014 한국품질경영학회 학술대회 Vol.2014 No.2
1. 목적 · Minitab Inc. 에서 클라우드 기반으로 개발한 몬테카를로 시뮬레이션 분석 툴인 Devize의 활용 방법 소개 2. 연구설계/ 방법론/ 정근방법 · Mintab의 실험계획법을 이용하여 반응식을 도출하고 도출된 반응식으로 몬테카를로 시뮬레이션을 적용하여 현재의 반응식으로 발생 가능한 공정능력 수준을 예측해 보고자 한다 시뮬레이션 툴로써 Minitab 사에서 출시 예정인 Devize를 활용하고자 한다. 3. 연구결과 · 공정에 영향을 미치는 입력 연수들에 대한 불확실성을 고려하여 보다 현실적인 공정능력을 예측해 볼 수 있으며 최적화를 통해서 우리의 공정의 개선 가능성을 판단해 볼 수 있다. 4. 실무적 시사점 · 간단한 툴을 이용하여 현업에서 쉽고 편리하게 시뮬레이션을 적용 업무에 활용해 볼 수 있음 5. 독창성/ 가치 · 기존의 평균 중심의 결정론적인 사고에서 시뮬레이션을 활용한 확률론적인 사고의 전환
Surajit Pathak,Alessia Rosaria Grillo,Melania Scarpa,Paola Brun,Renata D’Incà,Laura Nai,Antara Banerjee,Donatella Cavallo,Luisa Barzon,Giorgio Palù,Giacomo Carlo Sturniolo,Andrea Buda,Ignazio Castagli 생화학분자생물학회 2015 Experimental and molecular medicine Vol.47 No.-
Abnormal levels of microRNA (miR)-155, which regulate inflammation and immune responses, have been demonstrated in the colonic mucosa of patients with inflammatory bowel diseases (IBD), although its role in disease pathophysiology is unknown. We investigated the role of miR-155 in the acquisition and maintenance of an activated phenotype by intestinal myofibroblasts (IMF), a key cell population contributing to mucosal damage in IBD. IMF were isolated from colonic biopsies of healthy controls, ulcerative colitis (UC) and Crohn’s disease (CD) patients. MiR-155 in IMF was quantified by quantitative reverse transcription-PCR in basal condition and following exposure to TNF-α, interleukin (IL)-1β, lipopolysaccharide (LPS) or TGF-β1. The effects of miR-155 mimic or inhibitor transfection on cytokine release and suppressor of cytokine signaling 1 (SOCS1) expression were assessed by enzyme-linked immunosorbent assay and western blot, respectively. Regulation of the target gene SOCS1 expression by miR-155 was assessed using luciferase reporter construct. We found that miR-155 was significantly upregulated in UC as compared with control- and CD-derived IMF. Moreover, TNF-α and LPS, but not TGF-β1 and IL-1β, significantly increased miR-155 expression in IMF. Ectopic expression of miR-155 in control IMF augmented cytokines release, whereas it downregulated SOCS1 expression. MiR-155 knockdown in UC-IMF reduced cytokine production and enhanced SOCS1 expression. Luciferase reporter assay demonstrated that miR-155 directly targets SOCS1. Moreover, silencing of SOCS1 in control IMF significantly increased IL-6 and IL-8 release. In all, our data suggest that inflammatory mediators induce miR-155 expression in IMF of patients with UC. By downregulating the expression of SOCS1, miR-155 wires IMF inflammatory phenotype.
Fast Video Fire Detection Using Luminous Smoke and Textured Flame Features
Ince, Ibrahim Furkan,Yildirim, Mustafa Eren,Salman, Yucel Batu,Ince, Omer Faruk,Lee, Geun-Hoo,Park, Jang-Sik Korean Society for Internet Information 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.
Experimental design approach for ultra-fast nickel removal by novel bio-nanocomposite material
Ince, Olcay K.,Aydogdu, Burcu,Alp, Hevidar,Ince, Muharrem Techno-Press 2021 Advances in nano research Vol.10 No.1
In the present study, novel chitosan coated magnetic magnetite (Fe3O4) nanoparticles were successfully biosynthesized from mushroom, Agaricus campestris, extract. The obtained bio-nanocomposite material was used to investigate ultra-fast and highly efficient for removal of Ni2+ ions in a fixed-bed column. Chitosan was treated as polyelectrolyte complex with Fe3O4 nanoparticles and a Fungal Bio-Nanocomposite Material (FBNM) was derived. The FBNM was characterized by using X-Ray Diffractometer (XRD), Scanning Electron Microscopy-Energy Dispersive X-Ray Spectroscopy (SEM-EDS), Fourier Transform Infrared spectra (FTIR) and Thermogravimetric Analysis (TGA) techniques and under varied experimental conditions. The influence of some important operating conditions including pH, flow rate and initial Ni2+ concentration on the uptake of Ni2+ solution was also optimized using a synthetic water sample. A Central Composite Design (CCD) combined with Response Surface Modeling (RSM) was carried out to maximize Ni2+ removal using FBNM for adsorption process. A regression model was derived using CCD to predict the responses and analysis of variance (ANOVA) and lack of fit test was used to check model adequacy. It was observed that the quadratic model, which was controlled and proposed, was originated from experimental design data. The FBNM maximum adsorption capacity was determined as 59.8 mg g-1. Finally, developed method was applied to soft drinks to determine Ni2+ levels. Reusability of FBNM was tested, and the adsorption and desorption capacities were not affected after eight cycles. The paper suggests that the FBNM is a promising recyclable nanoadsorbent for the removal of Ni2+ from various soft drinks.
Human activity recognition with analysis of angles between skeletal joints using a RGB-depth sensor
Ince, Omer Faruk,Ince, Ibrahim Furkan,Yildirim, Mustafa Eren,Park, Jang Sik,Song, Jong Kwan,Yoon, Byung Woo Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.1
Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.
Human Identification Using Video-Based Analysis of the Angle Between Skeletal Joints
Omer Faruk Ince,Ibrahim Furkan Ince,Mustafa Eren Yildirim,Jang Sik Park(박장식),Jong-Kwan Song(송종관) 제어로봇시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.3
In this study, the angles between gait-relevant joints are considered a robust and differential feature set. The aim of this paper is to develop an approach that identifies a person’s gait cycle using body-joint information. The proposed approach acquires six different joint angle measurements using an RGB depth sensor, and then stores these in a queue-attribute collection. A genetic algorithm is then applied to reduce the number of features from 120 to 43. Following this, the data is trained with both a random forest classifier (RFC) and a K-nearest neighbor (KNN) algorithm . An average accuracy of 95.64% and 91.98% for gait analysis and identification with RFC and KNN algorithms respectively.
Fast Video Fire Detection Using Luminous Smoke and Textured Flame Features
( Ibrahim Furkan Ince ),( Mustafa Eren Yildirim ),( Yucel Batu Salman ),( Omer Faruk Ince ),( Geun-hoo Lee ),( Jang-sik Park ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12
In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.