• Korean Journal of Radiology
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• v.24 no.6
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• Tunnel and Underground Space
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• v.34 no.5
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• pp.527-542
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• 2024

A fundamental study was carried out on automatic steering control necessary for autonomous operation of shield TBMs in the future. It outlines and proposes theories and algorithms for predicting the strokes of articulation jacks used in a shield TBM and calculating the three-dimensional path coordinates of the shield TBM based on these predictions. To predict the strokes of articulation jacks, two methods were applied: a machine learning model based on the random forest regressor, and an iterative calculation method to satisfy a preset allowable error. For the iterative calculation, optimization methods were applied to reduce computation time. The mean and variance of the relative errors from the iterative calculation with allowable error were found to be relatively smaller than the predictions of the machine learning model. However, even with optimization methods applied, the iterative calculation method showed limitations in the allowable error that could be applied in terms of computation time. Therefore, it would be better to apply the machine learning model when real-time calculation speed is crucial. On the other hand, when pre-calculated results can be used during construction, the iterative calculation can be applied to achieve higher accuracy.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• Journal of the Korean Physical Society
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• v.80
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• pp.516-525
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• 2022

This study is to assess the clinical use of commercial PerFRACTION^TM for patient-specific quality assurance of volumetric-modulated arc therapy. Forty-six pretreatment verification plans for patients treated using a TrueBeam STx linear accelerator for lesions in various treatment sites such as brain, head and neck (H&N), prostate, and lung were included in this study. All pretreatment verification plans were generated using the Eclipse treatment planning system (TPS). Dose distributions obtained from electronic portal imaging device (EPID), ArcCHECK^TM, and two-dimensional (2D)/three-dimensional (3D) PerFRACTION^TM were then compared with the dose distribution calculated from the Eclipse TPS. In addition, the correlation between the plan complexity (the modulation complexity score and the leaf travel modulation complexity score) and the gamma passing rates (GPRs) of each quality assurance (QA) system was evaluated by calculating Spearman's rank correlation coefficient (r_s) with the corresponding p-values. The gamma passing rates of 46 patients analyzed with the 2D/3D PerFRACTION^TM using the 2%/2 mm and 3%/3 mm criteria showed almost similar trends to those analyzed with the Portal dose imaging prediction (PDIP) and ArcCHECK^TM except for those analyzed with ArcCHECK^TM using the 2%/2 mm criterion. Most of weak or moderate correlations between GPRs and plan complexity were observed for all QA systems. The trend of mean rs between GPRs using PDIP and 2D/3D PerFRACTION^TM for both criteria and plan complexity indices as in the GPRs analysis was significantly similar for brain, prostate, and lung cases with lower complexity compared to H&N case. Furthermore, the trend of mean rs for 2D/3D PerFRACTION^TM for H&N case with high complexity was similar to that of ArcCHECK^TM and slightly lower correlation was observed than that of PDIP. This work showed that the performance of 2D/3D PerFRACTION^TM for pretreatment patient-specific QA was almost comparable to that of PDIP, although there was small difference from ArcCHECK^TM for some cases. Thus, we found that the PerFRACTION^TM is a suitable QA system for pretreatment patient-specific QA in a variety of treatment sites.

• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.657-668
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• 2000

The purpose of this study was to evaluate the amount and interrelationship of the soft tissue of nose and maxillary changes and to identify the nasal morphologic features that indicate susceptibility to nasal deflection in such a manner that they would be useful in presurgical prediction of nasal changes after maxillary advancement surgery in skeletal Class III malocclusion. The sample consisted of 25 adult patients (13 males and 12 females) who had severe anteroposterior skeletal discrepancy. The patients had received presurgical orthodontic treatment. They underwent a Le Fort I advancement osteotomy, rigid internal fixation, alar cinch suture and V-Y advancement lip closure. The presurgical and postsurgical lateral cephalograms and lateral and frontal facial photographs were evaluated. The computerized statistical analysis was carried out. Soft tissue of nose change to h point change ratios were calculated by regression equations. The results were as follows 1. The correlation of maxillary hard tissue horizontal changes and nasal soft tissue vortical changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.228 at ANt, 0.257 at SNt. 2. The correlation of maxillary hard tissue and nasal soft tissue horizontal changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.484 at ANt, 0.431 at SNt, 0.806 at Sn. 3. The correlation of maxillary hard tissue horizontal changes and width changes of ala of nose were high and the ${\beta}_0$ lot alar base width ratio to ADV were 0.002. 4. The DRI, Prominence of nose, Pre-Op CA is not a quantitative measure that can be used clinically to improve the predictability of vertical and horizontal nasal tip deflection. In this study, increases in nasal tip projection and anterosuperior rotation occur when there is an anterior vector of maxillary movement. These nasal changes were Quantitatively correlated to magnitude of maxillary(A point) movement.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.165-190
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• 2008

The degree of informational asymmetry relating to the expiration of index derivatives is usually increased as an expiration day of index derivatives approaches. The increase in the degree of informational asymmetry may have some effects on trading behavior of investors. To examine what the effects look like, 'life cycle of index derivatives' in this study is defined as three adjacent periods around expiration day: pre-expiration period(a week before the expiration day), post-expiration period(a week after the expiration day), and remaining period. It is inspected whether stock investor's trading behavior is changed according to the life cycle of KOSPI200 derivatives and what the reason of the changing behavior is. We have four results. First, trading behavior of each investor group is categorized into three patterns: ㄱ-pattern, L-pattern and U-pattern. The level of trading activity is low for pre-expiration period and normal for other periods in the ㄱ-pattern. L-pattern means that the level of trading activity is high for post-expiration period and normal for other periods. In the U-pattern, the trading activity is reduced for remaining period compared to other periods. Second, individual investors have ㄱ-pattern of trading large stocks according to the life cycle of KOSPI200 index futures while they show U-pattern according to the life cycle of KOSPI200 index options. Their trading behavior is consistent with the prediction of Foster and Viswanathan(1990)'s model for strategic liquidity investors. Third, trading pattern of foreign investors in relation to life cycle of index derivatives is partially explained by the model, but trading pattern of institutional investors has nothing to do with the predictions of the model.

• The Korean Journal of Pesticide Science
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• v.12 no.1
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• pp.67-73
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• 2008

The dissipation patterns of a boscalid in cucumber under greenhouse condition was investigated to establish pre-harvest residue limit (PHRL) and biological half-life. Initial concentration of boscalid in cucumber at standard application rate was $7.29\;mg\;kg^{-1}$ and decreased to $0.04\;mg\;kg^{-1}$ after 15 days with half-life of 1.9 day, while the initial concentration was $14.69\;mg\;kg^{-1}$ and decreased to $0.11\;mg\;kg^{-1}$ after same period with half lift of 2.0 day at double application rate. PHRL was suggested by prediction curve derived from the decay curve of boscalid at double rate treatment. For example, $10.39\;mg\;kg^{-1}$ was calculated for 10 days before harvest, and $1.73\;mg\;kg^{-1}$ for 5 days. Dilution effect was major factor far the decrease of boscalid residue due to fast increasement of weight of cucumber during cultivation. Final residues level of boscalid was predicted based on the dissipation curve and guideline on safe use, when boscalid was used to control powdery mildew and gray mold. At standard rate application, $1.26\;mg\;kg^{-1}$ and $1.33\;mg\;kg^{-1}$ were calculated as final residue levels for control powdery mildew and gray mold, respectively, which are above the MRL(Meximum Residue Limit).

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.