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Seokjun Seo,Donghyeon Ahn,Seongil Heo,Heungseob Kim 한국산업경영시스템학회 2021 한국산업경영시스템학회 학술대회 Vol.2021 No.춘계
This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.
Areas of polygons with vertices from Lucas sequences on a plane
SeokJun Hong,SiHyun Moon,박호,SeoYeon Park,SoYoung Seo 대한수학회 2023 대한수학회논문집 Vol.38 No.3
Area problems for triangles and polygons whose vertices have Fibonacci numbers on a plane were presented by A. Shriki, O. Liba, and S. Edwards et al. In 2017, V. P. Johnson and C. K. Cook addressed problems of the areas of triangles and polygons whose vertices have various sequences. This paper examines the conditions of triangles and polygons whose vertices have Lucas sequences and presents a formula for their areas.
Seo, Jindeok,Lim, Kyomuk,Lee, Sangmin,Ahn, Jaehyun,Hong, Seokjune,Yoo, Hyungjung,Jung, Sukwon,Park, Sunkil,Cho, Dong-Il Dan,Ko, Hyoungho The Institute of Electronics and Information Engin 2013 Journal of semiconductor technology and science Vol.13 No.1
We describe a neural stimulator front-end with arbitrary stimulation waveform generator and adaptive supply regulator (ASR) for visual prosthesis. Each pixel circuit generates arbitrary current waveform with 5 bit programmable amplitude. The ASR provides the internal supply voltage regulated to the minimum required voltage for stimulation. The prototype is implemented in $0.35{\mu}m$ CMOS with HV option and occupies $2.94mm^2$ including I/Os.
DeepFam: deep learning based alignment-free method for protein family modeling and prediction
Seo, Seokjun,Oh, Minsik,Park, Youngjune,Kim, Sun Oxford University Press 2018 Bioinformatics Vol.34 No.13
<P><B>Abstract</B></P><P><B>Motivation</B></P><P>A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and <I>k</I>-mer based methods. Nevertheless, existing methods have some limitations; <I>k</I>-mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed.</P><P><B>Results</B></P><P>In this paper, we introduce DeepFam, an alignment-free method that can extract functional information directly from sequences without the need of multiple sequence alignments. In extensive experiments using the Clusters of Orthologous Groups (COGs) and G protein-coupled receptor (GPCR) dataset, DeepFam achieved better performance in terms of accuracy and runtime for predicting functions of proteins compared to the state-of-the-art methods, both alignment-free and alignment-based methods. Additionally, we showed that DeepFam has a power of capturing conserved regions to model protein families. In fact, DeepFam was able to detect conserved regions documented in the Prosite database while predicting functions of proteins. Our deep learning method will be useful in characterizing functions of the ever increasing protein sequences.</P><P><B>Availability and implementation</B></P><P>Codes are available at https://bhi-kimlab.github.io/DeepFam.</P>
쾌삭 303계 스테인리스강 소형 압연 선재 제조 공정의 생산품질 예측 모형
서석준(Seokjun Seo),김흥섭(Heungseob Kim) 한국산업경영시스템학회 2021 한국산업경영시스템학회지 Vol.44 No.4
This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the develop-ment of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on -fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, -score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.
Jindeok Seo,Kyomuk Lim,Sangmin Lee,Jaehyun Ahn,Seokjune Hong,Hyungjung Yoo,Sukwon Jung,Sunkil Park,Dong-il Dan Cho,Hyoungho Ko 대한전자공학회 2013 Journal of semiconductor technology and science Vol.13 No.1
We describe a neural stimulator front-end with arbitrary stimulation waveform generator and adaptive supply regulator (ASR) for visual prosthesis. Each pixel circuit generates arbitrary current waveform with 5 bit programmable amplitude. The ASR provides the internal supply voltage regulated to the minimum required voltage for stimulation. The prototype is implemented in 0.35 μm CMOS with HV option and occupies 2.94 mm₂ including I/Os.