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Sunjin Yim,Sungchul Kim,Inhwan Kim,Jae-Woo Park,Jin-Hyoung Cho,Mihee Hong,Kyung-Hwa Kang,Minji Kim,Su-Jung Kim,Yoon-Ji Kim,Young Ho Kim,Sung-Hoon Lim,Sang Jin Sung,Namkug Kim,Seung-Hak Baek 대한치과교정학회 2022 대한치과교정학회지 Vol.52 No.1
Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.
Kim Jisook,Bae Inhwan,Song Jiyoung,Kim Younghoon,Ahn Younggil,Park Hyun‐Ju,Kim Ha Hyung,Kim Dae Kyong 대한화학회 2021 Bulletin of the Korean Chemical Society Vol.42 No.6
Apoptosis inhibitor (IAP) proteins are overexpressed in many cancers and implicated in tumor growth, so the development of antagonist that disrupts with the binding of IAP to their partner protein is a promising therapeutic strategy. In an effort to increase cellular activity and improve favorable drug-like properties, we newly designed and synthesized monovalent analogues based on imidazopyrazinone structure of 9. Optimization of cellular potency led to the identification of 17, which showed increase of submicromolar activity (GI50 = 234 nM) and caspase-3 activation (6.3-fold) in MDA-MB-231 breast cancer cells. These findings clearly show the potential for 17 as a promising monovalent antagonist for the development of an effective anticancer treatment.
김덕호(Deokho Kim),김인환(Inhwan Kim),김영일(Youngil Kim),김용달(Yongdall Kim),김호경(Hokyong Kim) 한국자동차공학회 2020 한국 자동차공학회논문집 Vol.28 No.3
Automotive tuning is the process of modifying a car or its configurations to improve its performance or appearance. Although automotive tuning was subject to strict regulations until recently, the Korean government has been simplifying the tuning approval process, which includes the reduction of the tuning approval scope and the expansion of the exclusion cases to the extent that these do not disrupt safe driving and cause damage to the environment with the activation of the tuning industry in Korea. If the growth potential of the automotive tuning industry is increased and the automotive tuning market is activated for an expansion in the future, there will be more jobs around small manufacturers supplying tuning parts. The aim of this study is to identify the problems related to the automotive tuning approval procedure of Korea, and to suggest approaches in solving these problems by analyzing the automotive tuning industry in Korea and comparing it to that of the advanced countries. In addition, this study will identify the problems related to the automotive parts certification system and suggest an inspection and management method for automotive tuning using new technologies.
Mihee Hong,Inhwan Kim,Jin-Hyoung Cho,Kyung-Hwa Kang,Minji Kim,Su-Jung Kim,Yoon-Ji Kim,Sang-Jin Sung,Young Ho Kim,Sung-Hoon Lim,Namkug Kim,Seung-Hak Baek 대한치과교정학회 2022 대한치과교정학회지 Vol.52 No.4
Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent twojaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four timepoints (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.
Fault detection and classification of permanent magnet synchronous machine using signal injection
Inhwan Kim,Younghun Lee,Jaewook Oh,Namsu Kim 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.6
Condition monitoring of permanent magnet synchronous motors (PMSMs) and detecting faults such as eccentricity and demagnetization are essential for ensuring system reliability. Motor current signal analysis is the most commonly used precursor for detecting faults in the PMSM drive system. However, the current signature responds sensitively to the load and temperature of the motor, thereby making it difficult to monitor faults in real- applications. Therefore, in this study, a condition monitoring methodology that detects motor faults, including their classification with standstill conditions, is proposed. The objective is to detect and classify faults of PMSMs by using programmable inverter without additional sensors and systems for detection. Both DC and AC were applied through the d-axis of a three-phase motor, and the change in incremental inductance was investigated to detect and classify faults. Simulation with finite element analysis and experiments were performed on PMSMs in healthy conditions as well as with eccentricity and demagnetization faults. Based on the results obtained from experiments, the proposed method was confirmed to detect and classify types of faults, including their severity.
Kim, Inhwan,Lee, Kyulin,Cho, Gilsoo THE KOREAN FIBER SOCIETY 2016 FIBERS AND POLYMERS Vol. No.
Response surface methodology (RSM) is a collection of statistical and mathematical techniques, used for modeling and optimization. This study aimed to suggest the optimum treatment condition for minimizing fabric frictional sound and maximizing heat storage and release properties of combat uniform fabric treated with phase change materials (PCMs). Nine treatment conditions were determined by central composite design (CCD) of RSM. The independent variables were the concentration of PCMs (<TEX>$X_1$</TEX> : 6, 12, 18, 24, 30 %) and curing temperature (<TEX>$X_2$</TEX> : 95, 100, 105, 110, <TEX>$115^{\circ}C$</TEX>). The degree of increase in sound pressure level (SPL) of the treated specimen ranged from 1.84 to 8.971 %, demonstrating that the treatment caused a fabric frictional sound to be louder. The SPL increased significantly as concentration increased by 18 % and there was no significant effect of curing temperature on SPL. According to the analysis on the relationship between tensile properties and SPL, toughness (<TEX>$R^2=.706$</TEX>) was closely related to SPL, whereas tensile strength and elongation at break were not. The optimum treatment condition for minimizing fabric frictional sound and maximizing the heat storage and heat release properties was suggested. The regression models about SPL, heat of fusion (<TEX>${\Delta}H_f$</TEX>) and heat of crystallization (<TEX>${\Delta}H_c$</TEX>) were investigated with respect to two independent variables of treatment conditions, concentration and curing temperature. The optimum treatment condition in the model was concentration of 15.9 % and the curing temperature of <TEX>$113.6^{\circ}C$</TEX>. The predicted SPL and <TEX>${\Delta}H_f$</TEX> were 63.21 dB (<TEX>$R^2=0.99$</TEX>) and 4.70 J/g (<TEX>$R^2=0.95$</TEX>) respectively.