• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.85-92
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• 2023

In this paper, we propose a method for automatically generating story-based abbreviated summaries from long-form dramas and movies. From the shooting stage, the basic premise was to compose a frame with illusion of depth considering the golden division as well as focus on the object of interest to focus the viewer's attention in terms of content delivery. To consider how to extract the appropriate frames for this purpose, we utilized elemental techniques that have been utilized in previous work on scene and shot detection, as well as work on identifying focus-related blur. After converting the videos shared on YouTube to frame-by-frame, we divided them into a entire frame and three partial regions for feature extraction, and calculated the results of applying Laplacian operator and FFT to each region to choose the FFT with relative consistency and robustness. By comparing the calculated values for the entire frame with the calculated values for the three regions, the target frames were selected based on the condition that relatively sharp regions could be identified. Based on the selected results, the final frames were extracted by combining the results of an offline change point detection method to ensure the continuity of the frames within the shot, and an edit decision list was constructed to produce an abbreviated summary of 62.77% of the footage with F1-Score of 75.9%

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.893-901
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• 2023

The International Association Marine Aids to Navigation and Lighthouse Authorities (IALA) proposed guidelines for VTS manual operation in 2016 for safe and efficient operation of ship. The Korea Coast Guard (KCG) established and operated 19 VTS centers in ports and coastal waters across the country by 2022 based on the IALA VTS manual and VTS operator's education and training guidelines. In addition, IALA proposed the Inter-VTS Exchange Format (IVEF) Service recommendation (V-145), a standard for data exchange between VTS, in 2011 for efficient e-Navigation system services and safe and efficient VTS service support by VTS authorities. The IVEF service in a common framework for ship information exchange, and it presents seven basic IVEF service (BISs) models. VTS service providers can provide safer and more efficient VTS services by sharing VTS information on joint area using IVEF standards. Based on the BIS data, interaction, and interfacing models, this paper introduced the development of the cloud-based VTS integration services performed by the KCG and the results of the VTS integration platform test-bed for IVEF service implementation. In addition, the results of establishing a cloud VTS integrated platform test-bed for the implementation of IVEF service and implementing the main functions of IVEF service were presented.

• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.47-52
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• 2023

In order to efficiently maintain heat pipes operated by district heating operators, the facility history and damage history data built by the operator are used to identify key independent variables that are related to the occurrence of damage. Afterwards, the correlation with the frequency of damage was analyzed, and a basic model for estimating the frequency of damage was derived. Considering the correlation with the estimation model based on the use time currently being used by domestic and foreign district heating operators, a simple regression analysis basic model was presented as the independent variable with the highest correlation between continuous variables such as the use time, pipe diameter, burial depth, and insulation level of monitoring system, and the frequency of damage. The remaining independent variables were reflected as factors that modify and supplement the basic model. As a result of the analysis, as in previous research cases, it was confirmed that the analysis model between use time and frequency of damage had the highest correlation between the two variables and could be used as a basic model. Pipe diameter, burial depth, and insulation level of monitoring system information have also been confirmed to have a correlation with the frequency of damage, so they can be used as factors to supplement the basic model.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.3
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• pp.176-185
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• 2023

Facebow transfer is essential for accurately mounting a dental cast onto a semi-adjustable articulator. The precision of traditional analog facebow transfer is influenced by both the accuracy of the equipment used and the skill level of the operator. Considering that substantial positional deviations can adversely affect the quality of a fabricated dental prosthesis; it is critical to assess the positional accuracy of casts mounted using analog facebow transfer. This case report evaluates the linear and angular deviations of the occlusal plane for maxillary casts mounted through both analog facebow transfer and cone-beam computed tomography-based methods. The findings indicate that analog facebow transfer produced a linear deviation ranging from 3 to 16 mm and an angular deviation of the occlusal plane between 5 to 7 degrees. This case report confirms that, across two patients, analog facebow transfer can result in varying degrees of positional deviation, thereby potentially leading to inaccuracies in the fabrication of dental prostheses. These results suggest that, in clinical practice, the use of analog facebow transfer may yield significant deviations during the process of mounting maxillary casts.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.172-177
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• 2023

In this paper, 2-dimensional backtracking method for ultrasonic signals. Ultrasonic sensors are a common technology used in industrial fields as many studies have been conducted on distance measurement and indoor location tracking using transmission and reception devices in pairs. A method for tracking a signal of an arbitrary ultrasonic transmission device on a 2D plane using only a receiver of an ultrasonic signal is proposed. In order to track the ultrasonic signal, the receiver receives the signal by making at least three. The three receivers may calculate a direction and a distance using a time difference in which the ultrasound reception sound is reached. The existing method of tracking signal sources using ultrasonic waves has a problem of time synchronization of devices because the transceivers must be paired or installed independently for each sensor. In order to solve this problem, the distance of the ultrasonic receiver is minimized, and it is configured as one device. The sensor installed as one device may be processed by one operator, thereby solving the time synchronization problem. To increase time difference accuracy, high-speed 32-bit timers with high time resolution can be used to quickly calculate and track distances and directions.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.395-405
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• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Korean Journal of Radiology
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• v.23 no.8
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.827-837
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• 2023

Objective: To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). Materials and Methods: We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the oneweek CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). Results: Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). Conclusion: Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1067-1077
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• 2022

Objective: To determine whether Sonazoid-enhanced ultrasound (SZUS) was noninferior to SonoVue-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) using the same diagnostic criteria. Materials and Methods: This prospective, single-center, noninferiority study (NCT04847726) enrolled 105 at-risk participants (71 male; mean age ± standard deviation, 63 ± 11 years; range, 26-86 years) with treatment-naïve solid hepatic nodules (≥ 1 cm). All participants underwent same-day SZUS (experimental method) and SVUS (control method) for one representative nodule per participant. Images were interpreted by three readers (the operator and two independent readers). All malignancies were diagnosed histopathologically, while the benignity of other lesions was confirmed by follow-up stability or pathology. The primary endpoint was per-lesion diagnostic accuracy for HCC pooled across three readers using the conventional contrast-enhanced ultrasound diagnostic criteria, including arterial phase hyperenhancement followed by mild (assessed within 2 minutes after contrast injection) and late (≥ 60 seconds with a delay of 5 minutes) washout. The noninferiority delta was -10%p. Furthermore, different time delays were compared as washout criteria in SZUS, including delays of 2, 5, and > 10 minutes. Results: A total of 105 lesions (HCCs [n = 61], non-HCC malignancies [n = 19], and benign [n = 25]) were evaluated. Using the 5-minutes washout criterion, per-lesion accuracy of SZUS pooled across the three readers (72.4%; 95% confidence interval [CI], 64.1%-79.3%) was noninferior to that of SVUS (71.4%; 95% CI, 63.1%-78.6%), meeting the statistical criterion for non-inferiority (difference of 0.95%p; 95% CI, -3.8%p-5.7%p). The arterial phase hyperenhancement combined with the 5-minutes washout criterion showed the same sensitivity as that of the > 10-minutes criterion (59.0% vs. 59.0%, p = 0.989), and the specificities were not significantly different (90.9% vs. 86.4%, p = 0.072). Conclusion: SZUS was noninferior to SVUS for diagnosing HCC in at-risk patients using the same diagnostic criteria. No significant improvement in HCC diagnosis was observed by extending the washout time delay from 5 to 10 minutes.